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Pisa, Watchmaker, FOM, Hypercube, HotFrame, Templar, EasyLocal, iOpt, OptQuest, JDEAL, Optimization
Algorithm Toolkit, HeuristicLab, MAFRA, Localizer, GALIB, DREAM, Discropt, MALLBA, MAGMA, and UOF. There have been a number of publications on the support of parallel implementations, which was missing in this comparative study, particularly from the late 10s onwards.
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possible time. In practice, restrictions often have to be observed, e.g. by limiting the permissible sequence of work steps of a job through predefined workflows and/or with regard to resource utilisation, e.g. in the form of smoothing the energy demand. Popular metaheuristics for combinatorial problems include
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continuous or mixed-integer optimization. As such, metaheuristics are useful approaches for optimization problems. Several books and survey papers have been published on the subject. Literature review on metaheuristic optimization, suggested that it was Fred Glover who coined the word metaheuristics.
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Metaheuristics are also frequently applied to scheduling problems. A typical representative of this combinatorial task class is job shop scheduling, which involves assigning the work steps of jobs to processing stations in such a way that all jobs are completed on time and altogether in the shortest
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Most metaheuristics are search methods and when using them, the evaluation function should be subject to greater demands than a mathematical optimization. Not only does the desired target state have to be formulated, but the evaluation should also reward improvements to a solution on the way to the
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Especially since the turn of the millennium, many metaheuristic methods have been published with claims of novelty and practical efficacy. While the field also features high-quality research, many of the more recent publications have been of poor quality; flaws include vagueness, lack of conceptual
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problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem
468:
There are many candidate optimization tools which can be considered as a MOF of varying feature. The following list of 33 MOFs is compared and evaluated in detail in: Comet, EvA2, evolvica, Evolutionary::Algorithm, GAPlayground, jaga, JCLEC, JGAP, jMetal, n-genes, Open Beagle, Opt4j, ParadisEO/EO,
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A MOF can be defined as ââa set of software tools that provide a correct and reusable implementation of a set of metaheuristics, and the basic mechanisms to accelerate the implementation of its partner subordinate heuristics (possibly including solution encodings and technique-specific operators),
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problems and thus can no longer be solved exactly in an acceptable time from a relatively low degree of complexity. Metaheuristics then often provide good solutions with less computational effort than approximation methods, iterative methods, or simple heuristics. This also applies in the field of
1885:
Swan, Jerry; Adriaensen, Steven; Bishr, Mohamed; Burke, Edmund K.; Clark, John A.; De
Causmaecke, Patrick; Durillo, Juan JosĂ©; Hammond, Kevin; Hart, Emma; Johnson, Colin G.; Kocsis, Zoltan A.; Kovitz, Ben; Krawiec, Krzysztof; Martin, Simon; Merelo, Juan J.; Minku, Leandro L.; Ăzcan, Ender; Pappa,
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for hiding their lack of novelty behind an elaborate metaphor. As a result, a number of renowned scientists of the field have proposed a research agenda for the standardization of metaheuristics in order to make them more comparable, among other things. Another consequence is that the publication
363:
A very active area of research is the design of nature-inspired metaheuristics. Many recent metaheuristics, especially evolutionary computation-based algorithms, are inspired by natural systems. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing
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With population-based metaheuristics, the population itself can be parallelized by either processing each individual or group with a separate thread or the metaheuristic itself runs on one computer and the offspring are evaluated in a distributed manner per iteration. The latter is particularly
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Another large field of application are optimization tasks in continuous or mixed-integer search spaces. This includes, e.g., design optimization or various engineering tasks. An example of the mixture of combinatorial and continuous optimization is the planning of favourable motion paths for
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being solved and so may be usable for a variety of problems. Their use is always of interest when exact or other (approximate) methods are not available or are not expedient, either because the calculation time is too long or because, for example, the solution provided is too imprecise.
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represent the synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. An example of memetic algorithm is the use of a local search algorithm instead of or in addition to a basic
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useful if the computational effort for the evaluation is considerably greater than that for the generation of descendants. This is the case in many practical applications, especially in simulation-based calculations of solution quality.
1911:
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Metaheuristics are not problem-specific. However, they were often developed in relation to a problem class such as continuous or combinatorial optimization and then generalized later in some cases.
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Ganesan, T.; Elamvazuthi, I.; Ku Shaari, Ku Zilati; Vasant, P. (2013-03-01). "Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production".
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One approach is to characterize the type of search strategy. One type of search strategy is an improvement on simple local search algorithms. A well known local search algorithm is the
265:. Population-based approaches maintain and improve multiple candidate solutions, often using population characteristics to guide the search; population based metaheuristics include
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Nebro, Antonio J.; Barba-GonzĂĄlez, CristĂłbal; Nieto, JosĂ© GarcĂa; Cordero, JosĂ© A.; Montes, JosĂ© F. Aldana (2017-07-15), "Design and architecture of the jMetaISP framework",
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Ganesan, T.; Elamvazuthi, I.; Vasant, P. (2011-11-01). "Evolutionary normal-boundary intersection (ENBI) method for multi-objective optimization of green sand mould system".
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There are a wide variety of metaheuristics and a number of properties with respect to which to classify them. The following list is therefore to be understood as an example.
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Stefan Droste; Thomas Jansen; Ingo
Wegener (2002). "Optimization with Randomized Search Heuristics â The (A)NFL Theorem, Realistic Scenarios, and Difficult Functions".
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Parejo, José Antonio; Ruiz-Cortés, Antonio; Lozano, Sebastiån; Fernandez, Pablo (March 2012). "Metaheuristic optimization frameworks: a survey and benchmarking".
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Lim, Dudy; Ong, Yew-Soon; Jin, Yaochu; Sendhoff, Bernhard; Lee, Bu-Sung (May 2007). "Efficient
Hierarchical Parallel Genetic Algorithms using Grid computing".
1991:
Almeida, Francisco; Blesa
Aguilera, MarĂa J.; Blum, Christian; Moreno Vega, JosĂ© Marcos; PĂ©rez PĂ©rez, MelquĂades; Roli, Andrea; Sampels, Michael, eds. (2006).
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Many different metaheuristics are in existence and new variants are continually being proposed. Some of the most significant contributions to the field are:
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2331:"A metaheuristic for energy adaptive production scheduling with multiple energy carriers and its implementation in a real production system"
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Dueck, G.; Scheuer, T. (1990), "Threshold accepting: A general purpose optimization algorithm appearing superior to simulated annealing",
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searches. Single solution approaches focus on modifying and improving a single candidate solution; single solution metaheuristics include
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They can draw on domain-specific knowledge in the form of heuristics that are controlled by a higher-level strategy of the metaheuristic.
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Many metaheuristic ideas were proposed to improve local search heuristic in order to find better solutions. Such metaheuristics include
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GarcĂa-Valdez, Mario; Merelo, J.J. (2017-07-15), "evospace-js: asynchronous pool-based execution of heterogeneous metaheuristics",
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Gisele Lobo; Pesch, Erwin; GarcĂa-SĂĄnchez, Pablo; Schaerf, Andrea; Sim, Kevin; Smith, Jim; StĂŒtzle, Thomas; Wagner, Stefan (2015).
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for the optimal solution infeasible. Additionally, multidimensional combinatorial problems, including most design problems in
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Rastrigin, L.A. (1963). "The convergence of the random search method in the extremal control of a many parameter system".
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Raidl, GĂŒnther R. (2006), Almeida, Francisco; Blesa
Aguilera, MarĂa J.; Blum, Christian; Moreno Vega, JosĂ© Marcos (eds.),
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Gupta, Shubham; Abderazek, Hammoudi; Yıldız, BetĂŒl Sultan; Yildiz, Ali Riza; Mirjalili, Seyedali; Sait, Sadiq M. (2021).
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method which is used to find local optimums. However, hill climbing does not guarantee finding global optimum solutions.
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A hybrid metaheuristic is one that combines a metaheuristic with other optimization approaches, such as algorithms from
258:
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2474:"Optimization of a Micro Actuator Plate Using Evolutionary Algorithms and Simulation Based on Discrete Element Methods"
2249:"Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing"
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940:"Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems"
1530:, Lecture Notes in Computer Science, vol. 4030, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1â12,
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Jakob, Wilfried; Strack, Sylvia; Quinte, Alexander; Bengel, GĂŒnther; Stucky, Karl-Uwe; SĂŒĂ, Wolfgang (2013-04-22).
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1851:"A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics"
1008:. Mineola, N.Y: Dover Publ., corrected, unabridged new edition of the work published by Prentice-Hall in 1982.
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309:. Both components of a hybrid metaheuristic may run concurrently and exchange information to guide the search.
298:
266:
218:
4012:
3413:, Teytaud, Olivier (2010). "Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms".
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Igel, Christian, Toussaint, Marc (Jun 2003). "On classes of functions for which No Free Lunch results hold".
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1990: Moscato and
Fontanari, and Dueck and Scheuer, independently proposed a deterministic update rule for
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Pareto
Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II.
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2210:. Studies in Computational Intelligence. Vol. 128. Berlin, Heidelberg: Springer Berlin Heidelberg.
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Khalloof, Hatem; Mohammad, Mohammad; Shahoud, Shadi; Duepmeier, Clemens; Hagenmeyer, Veit (2020-11-02),
1633:"RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits"
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Quinte, Alexander; Jakob, Wilfried; Scherer, Klaus-Peter; Eggert, Horst (2002), Laudon, Matthew (ed.),
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The goal is to efficiently explore the search space in order to find optimal or nearâoptimal solutions.
110:, which state that there can be no metaheuristic that is better than all others for any given problem.
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Moscato, P.; Fontanari, J.F. (1990), "Stochastic versus deterministic update in simulated annealing",
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They can contain mechanisms that prevent them from getting stuck in certain areas of the search space.
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2671:, Lecture Notes in Computer Science, vol. 1803, Berlin, Heidelberg: Springer, pp. 330â341,
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1749:"On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms"
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206:. These metaheuristics can both be classified as local search-based or global search metaheuristics.
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2502:"Strategies for the Integration of Evolutionary/Adaptive Search with the Engineering Design Process"
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187:
99:
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Most literature on metaheuristics is experimental in nature, describing empirical results based on
3046:
Hastings, W.K. (1970). "Monte Carlo
Sampling Methods Using Markov Chains and Their Applications".
2626:. Studies in Computational Intelligence. Vol. 1069. Cham: Springer International Publishing.
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1970: Kernighan and Lin propose a graph partitioning method, related to variable-depth search and
4025:
3994:
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3590:
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Ashish Sharma (2022), Nature
Inspired Algorithms with Randomized Hypercomputational Perspective.
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which is a collective behavior of decentralized, self-organized agents in a population or swarm.
274:
226:
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2707:"Multiobjective Trajectory Planning of a 6D Robot based on Multiobjective Meta Heuristic Search"
2445:
Glover, F. (1986). "Future Paths for Integer Programming and Links to Artificial Intelligence".
1922:
882:. Vol. 57. Springer, International Series in Operations Research & Management Science.
160:
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Kirkpatrick, S.; Gelatt Jr., C.D.; Vecchi, M.P. (1983). "Optimization by Simulated Annealing".
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2557:
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2382:
2290:"An Ensemble of Meta-Heuristics for the Energy-Efficient Blocking Flowshop Scheduling Problem"
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2006:
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665:
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568:
536:
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313:
222:
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Kernighan, B.W.; Lin, S. (1970). "An efficient heuristic procedure for partitioning graphs".
2665:"Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM"
1410:
Colorni, Alberto; Dorigo, Marco; Maniezzo, Vittorio (1991), Varela, F.; Bourgine, P. (eds.),
412:
or combinations thereof. In combinatorial optimization, an optimal solution is sought over a
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1610:"Classification of Metaheuristics and Design of Experiments for the Analysis of Components"
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1967:
1943:
1772:. Genetic Algorithms and Evolutionary Computation. Vol. 1. Boston, MA: Springer US.
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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference, Companion
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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference, Companion
2501:
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which are necessary to solve a particular problem instance using techniques providedââ.
4117:
4002:
3889:
3823:
3794:
3567:
3125:
2428:
2405:
Glover, Fred (1977). "Heuristics for Integer programming Using Surrogate Constraints".
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1033:
729:"Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data"
603:
575:
75:
3503:
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2063:"Ant colony system: a cooperative learning approach to the traveling salesman problem"
970:
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with the algorithms. But some formal theoretical results are also available, often on
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4275:
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3299:
3265:
3139:
Mercer, R.E.; Sampson, J.R. (1978). "Adaptive search using a reproductive metaplan".
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1075:"Evolution strategies for continuous optimization: A survey of the state-of-the-art"
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1997:. Lecture Notes in Computer Science. Vol. 4030. Berlin, Heidelberg: Springer.
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805:
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can be found on some class of problems. Many metaheuristics implement some form of
3382:
3230:
2190:
1992:
840:"Metaheuristics in combinatorial optimization: Overview and conceptual comparison"
777:
Bianchi, Leonora; Marco Dorigo; Luca Maria Gambardella; Walter J. Gutjahr (2009).
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schemes to concurrent search runs that interact to improve the overall solution.
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2706:
2513:
2347:
2330:
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1850:
1817:
1683:"Optimize railway crew scheduling by using modified bacterial foraging algorithm"
1034:"Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review"
364:
systems to deal with complex computational problems. Such metaheuristics include
106:
and the possibility of finding the global optimum. Also worth mentioning are the
4300:
3682:
3006:
Rechenberg, Ingo (1965). "Cybernetic Solution Path of an Experimental Problem".
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Akan, Taymaz; Anter, Ahmed M.; Etaner-Uyar, A. Ćima; Oliva, Diego, eds. (2023).
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to run multiple metaheuristic searches in parallel; these may range from simple
191:
2165:
2011 IEEE International Conference on Control System, Computing and Engineering
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955:
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A large number of more recent metaphor-inspired metaheuristics have started to
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2765:
2631:
2596:
2553:
2215:
2062:
2037:
1777:
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1585:
1568:
1523:
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1215:
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Other global search metaheuristic that are not local search-based are usually
17:
3444:
3374:
3307:
3067:
2984:
2971:
Nelder, J.A.; Mead, R. (1965). "A simplex method for function minimization".
2773:
2711:
International Conference on Network, Communication and Computing (ICNCC 2018)
2676:
2478:
International Conference on Modeling and Simulation of Microsystems: MSM 2002
2274:
2115:
1706:
1656:
1608:
Birattari, Mauro; Paquete, Luis; StĂŒtzle, Thomas; Varrentrapp, Klaus (2001).
1594:
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Metaheuristics are used for all types of optimization problems, ranging from
388:
guidelines of a number of scientific journals have been adapted accordingly.
3553:
3167:
2855:
2796:
2718:
1862:
1387:
489:
3320:
Wolpert, D.H.; Macready, W.G. (1995). "No free lunch theorems for search".
3222:
1855:
Proc. of the 12th Int. Conf. on Management of Digital EcoSystems (MEDES'20)
1174:"Future paths for integer programming and links to artificial intelligence"
553:
1970: Cavicchio proposes adaptation of control parameters for an optimizer.
3093:. University of Michigan, Computer and Communication Sciences Department.
2207:
Metaheuristics for Scheduling in Industrial and Manufacturing Applications
855:
3702:
2329:
Grosch, Benedikt; Weitzel, Timm; Panten, Niklas; Abele, Eberhard (2019).
2288:
Kizilay, Damla; Tasgetiren, M. Fatih; Pan, Quan-Ke; SĂŒer, GĂŒrsel (2019).
2114:
TomoiagÄ B, ChindriĆ M, Sumper A, Sudria-Andreu A, Villafafila-Robles R.
151:
Modern metaheuristics often use the search history to control the search.
3098:
2500:
Parmee, I. C. (1997), Dasgupta, Dipankar; Michalewicz, Zbigniew (eds.),
2078:
1231:
1162:
X. S. Yang, Metaheuristic optimization, Scholarpedia, 6(8):11472 (2011).
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Metaheuristic algorithms are approximate and usually non-deterministic.
2914:
Barricelli, N.A. (1954). "Esempi numerici di processi di evoluzione".
779:"A survey on metaheuristics for stochastic combinatorial optimization"
3152:
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Cavicchio, D.J. (1970). "Adaptive search using simulated evolution".
2949:
2265:
2248:
2002:
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114:
elaboration, poor experiments, and ignorance of previous literature.
3357:
1471:"Constrained Combinatorial Optimization with an Evolution Strategy"
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397:
target in order to support and accelerate the search process. The
159:
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50:
designed to find, generate, tune, or select a heuristic (partial
1804:
Sudholt, Dirk (2015), Kacprzyk, Janusz; Pedrycz, Witold (eds.),
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Jarboui, Bassem; Siarry, Patrick; Teghem, Jacques, eds. (2013).
585:
for tuning an optimizer's parameters by using another optimizer.
481:
1952: Robbins and Monro work on stochastic optimization methods.
420:
where the search-space of candidate solutions grows faster than
401:
of evolutionary or memetic algorithms can serve as an example.
289:
and bacterial foraging algorithm are examples of this category.
132:
Techniques which constitute metaheuristic algorithms range from
4257:
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3936:
3864:
3650:
3600:
3584:
1106:
Genetic Algorithms in Search, Optimization and Machine Learning
2585:
Sanchez, Ernesto; Squillero, Giovanni; Tonda, Alberto (2012).
1469:
Nissen, Volker; Krause, Matthias (1994), Reusch, Bernd (ed.),
1416:
Conf. Proc. of ECAL91 - European Conference on Artificial Life
3848:
3520:
Sörensen, Kenneth; Sevaux, Marc; Glover, Fred (2017-01-16).
2026:
Neri, Ferrante; Cotta, Carlos; Moscato, Pablo, eds. (2012).
1912:"Journal of Heuristic Policies on Heuristic Search Research"
1326:"Why we fell out of love with algorithms inspired by nature"
436:, which also makes them infeasible for exhaustive search or
126:
Metaheuristics are strategies that guide the search process.
122:
These are properties that characterize most metaheuristics:
1216:"Self-adaptive mutations may lead to premature convergence"
432:
such as form-finding and behavior-finding, suffer from the
1073:
Li, Zhenhua; Lin, Xi; Zhang, Qingfu; Liu, Hailin (2020).
2400:
2398:
1133:
1131:
1129:
1127:
1125:
82:, so that the solution found is dependent on the set of
2094:"Memetic Algorithms for the Traveling Salesman Problem"
1004:
Papadimitriou, Christos H.; Steiglitz, Kenneth (1998).
492:
process and uses them on general optimization problems.
245:
Another classification dimension is single solution vs
3169:
A Learning System Based on Genetic Adaptive Algorithms
2368:
2366:
833:
831:
829:
827:
825:
823:
821:
819:
817:
815:
727:
R. Balamurugan; A.M. Natarajan; K. Premalatha (2015).
521:
to converge to non-stationary points on some problems.
90:, there are many problems that belong to the class of
54:) that may provide a sufficiently good solution to an
2545:
Applications of Metaheuristics in Process Engineering
1888:"A Research Agenda for Metaheuristic Standardization"
1742:
1740:
1006:
Combinatorial Optimization: Algorithms and Complexity
424:
as the size of the problem increases, which makes an
1637:
IEEE Transactions on Instrumentation and Measurement
4212:
4173:
4139:
4128:
4086:
4021:
3993:
3979:
3949:
3898:
3877:
3822:
3770:
3733:
3710:
3701:
3663:
3023:
Artificial Intelligence through Simulated Evolution
2506:
Evolutionary Algorithms in Engineering Applications
167:
of the different classifications of metaheuristics.
2588:Industrial Applications of Evolutionary Algorithms
2508:, Berlin, Heidelberg: Springer, pp. 453â477,
2374:
1812:, Berlin, Heidelberg: Springer, pp. 929â959,
1769:Efficient and Accurate Parallel Genetic Algorithms
1734:, PhD thesis, Politecnico di Milano, Italie, 1992.
1264:International Transactions in Operational Research
873:
871:
869:
867:
865:
3008:Royal Aircraft Establishment, Library Translation
2705:Pholdee, Nantiwat; Bureerat, Sujin (2018-12-14),
2669:Real-World Applications of Evolutionary Computing
2663:Blume, Christian (2000), Cagnoni, Stefano (ed.),
2623:Engineering Applications of Modern Metaheuristics
2542:Valadi, Jayaraman; Siarry, Patrick, eds. (2014).
2440:
2438:
349:Nature-inspired and metaphor-based metaheuristics
1440:"Ant colony optimization for continuous domains"
1038:Archives of Computational Methods in Engineering
1810:Springer Handbook of Computational Intelligence
1418:, Amsterdam: Elsevier Publ., pp. 134â142,
1253:
1251:
628:which accelerated the search. This led to the
3612:
2067:IEEE Transactions on Evolutionary Computation
1919:Journal of Heuristics - Submission guidelines
1732:Optimization, Learning and Natural Algorithms
1681:Pang, Shinsiong; Chen, Mu-Chen (2023-06-01).
1376:"A new optimizer using particle swarm theory"
1220:IEEE Transactions on Evolutionary Computation
1140:Metaheuristics: from design to implementation
772:
770:
768:
766:
764:
8:
3465:: CS1 maint: multiple names: authors list (
3395:: CS1 maint: multiple names: authors list (
3021:Fogel, L.; Owens, A.J.; Walsh, M.J. (1966).
2377:Adaptation in Natural and Artificial Systems
2061:Dorigo, M.; Gambardella, L.M. (April 1997).
213:metaheuristics. Such metaheuristics include
2548:. Cham: Springer International Publishing.
2204:Xhafa, Fatos; Abraham, Ajith, eds. (2008).
385:attract criticism in the research community
4254:
4170:
4136:
4083:
4070:
3990:
3946:
3933:
3874:
3861:
3707:
3660:
3647:
3619:
3605:
3597:
1412:"Distributed Optimization by Ant Colonies"
3554:https://doi.org/10.1016/j.ins.2022.05.020
3493:
3426:
3356:
3204:
2898:
2418:
2346:
2305:
2264:
1584:
1524:"A Unified View on Hybrid Metaheuristics"
1275:
1239:
1049:
744:
488:carries out the first simulations of the
273:. Another category of metaheuristics is
136:procedures to complex learning processes.
74:, metaheuristics do not guarantee that a
3853:Optimization computes maxima and minima.
1567:Glover, Fred; Sörensen, Kenneth (2015).
1444:European Journal of Operational Research
1438:Socha, Krzysztof; Dorigo, Marco (2008).
909:Metaheuristics for production scheduling
416:search-space. An example problem is the
359:List of metaphor-inspired metaheuristics
3172:(PhD Thesis). University of Pittsburgh.
2092:Merz, Peter; Freisleben, Bernd (2002).
1324:Brownlee, Alexander; Woodward, John R.
878:Glover, F.; Kochenberger, G.A. (2003).
719:
3458:
3388:
1753:Caltech Concurrent Computation Program
1687:Computers & Industrial Engineering
1299:
969:Brucker, Peter; Knust, Sigrid (2012).
838:Blum, Christian; Roli, Andrea (2003).
448:by Holland et al., scatter search and
4049:Principal pivoting algorithm of Lemke
3527:. In MartĂ, Rafael; Panos, Pardalos;
2850:, New York: ACM, pp. 1239â1246,
2791:, New York: ACM, pp. 1202â1208,
2747:
2745:
1844:
1842:
1799:
1797:
1676:
1674:
1562:
1560:
1319:
1317:
1260:"Metaheuristicsâthe metaphor exposed"
646:1995: Wolpert and Macready prove the
460:Metaheuristic Optimization Frameworks
7:
1027:
1025:
933:
931:
929:
901:
899:
293:Hybridization and memetic algorithms
241:Single-solution vs. population-based
3587:forum for researchers in the field.
2880:"A Stochastic Approximation Method"
581:1978: Mercer and Sampson propose a
333:is one that uses the techniques of
3693:Successive parabolic interpolation
3126:10.1002/j.1538-7305.1970.tb01770.x
2821:Future Generation Computer Systems
2429:10.1111/j.1540-5915.1977.tb01074.x
1806:"Parallel Evolutionary Algorithms"
1374:Eberhart, R.; Kennedy, J. (1995),
1079:Swarm and Evolutionary Computation
408:through mixed integer problems to
237:and bacterial foraging algorithm.
25:
4013:Projective algorithm of Karmarkar
3322:Technical Report SFI-TR-95-02-010
2887:Annals of Mathematical Statistics
2447:Computers and Operations Research
1181:Computers and Operations Research
595:1983: Kirkpatrick et al. propose
4008:Ellipsoid algorithm of Khachiyan
3911:Sequential quadratic programming
3748:BroydenâFletcherâGoldfarbâShanno
3280:Journal of Computational Physics
2381:. University of Michigan Press.
1511:Classification of metaheuristics
975:. Berlin, Heidelberg: Springer.
944:Expert Systems with Applications
3593:Source of some implementations.
2878:Robbins, H.; Monro, S. (1951).
2118:Energies. 2013; 6(3):1439â1455.
733:Applied Artificial Intelligence
558:prohibition-based (tabu) search
3966:Reduced gradient (FrankâWolfe)
3345:Information Processing Letters
2150:10.1016/j.apenergy.2012.09.059
2029:Handbook of Memetic Algorithms
1108:. Kluwer Academic Publishers.
175:Local search vs. global search
1:
4296:Spiral optimization algorithm
3916:Successive linear programming
3522:"A History of Metaheuristics"
3504:10.1016/s0304-3975(02)00094-4
3367:10.1016/S0020-0190(03)00222-9
3114:Bell System Technical Journal
2954:Automation and Remote Control
2931:Automation and Remote Control
1351:Evolution and optimum seeking
1172:Glover, Fred (January 1986).
746:10.1080/08839514.2015.1016391
548:MetropolisâHastings algorithm
287:social cognitive optimization
4034:Simplex algorithm of Dantzig
3906:Augmented Lagrangian methods
3570:and Kenneth Sörensen (ed.).
3482:Theoretical Computer Science
3300:10.1016/0021-9991(90)90201-B
3266:10.1016/0375-9601(90)90166-L
3215:10.1126/science.220.4598.671
2833:10.1016/j.future.2006.10.008
2514:10.1007/978-3-662-03423-1_25
2459:10.1016/0305-0548(86)90048-1
2348:10.1016/j.procir.2019.01.043
2307:10.1016/j.promfg.2020.01.352
1818:10.1007/978-3-662-43505-2_46
1349:Schwefel, Hans-Paul (1995).
1193:10.1016/0305-0548(86)90048-1
610:, first mention of the term
546:1970: Hastings proposes the
321:in evolutionary algorithms.
259:variable neighborhood search
235:rider optimization algorithm
200:variable neighborhood search
2173:10.1109/ICCSCE.2011.6190501
1483:10.1007/978-3-642-79386-8_5
1091:10.1016/j.swevo.2020.100694
418:travelling salesman problem
378:particle swarm optimization
283:particle swarm optimization
271:particle swarm optimization
231:particle swarm optimization
4355:
1456:10.1016/j.ejor.2006.06.046
1330:The Conversation (website)
1306:: CS1 maint: url-status (
1258:Sörensen, Kenneth (2015).
1051:10.1007/s11831-021-09694-4
956:10.1016/j.eswa.2021.115351
880:Handbook of metaheuristics
410:combinatorial optimization
352:
88:combinatorial optimization
4313:
4266:
4253:
4237:Pushârelabel maximum flow
4082:
4069:
4039:Revised simplex algorithm
3945:
3932:
3873:
3860:
3846:
3659:
3646:
3437:10.1007/s00453-008-9244-5
2766:10.1007/s00500-011-0754-8
2713:, ACM, pp. 352â356,
2632:10.1007/978-3-031-16832-1
2597:10.1007/978-3-642-27467-1
2554:10.1007/978-3-319-06508-3
2216:10.1007/978-3-540-78985-7
2038:10.1007/978-3-642-23247-3
1857:, ACM, pp. 124â131,
1778:10.1007/978-1-4615-4369-5
1766:CantĂș-Paz, Erick (2001).
1699:10.1016/j.cie.2023.109218
1586:10.4249/scholarpedia.6532
981:10.1007/978-3-642-23929-8
798:10.1007/s11047-008-9098-4
495:1963: Rastrigin proposes
76:globally optimal solution
36:mathematical optimization
3762:Symmetric rank-one (SR1)
3743:BerndtâHallâHallâHausman
2677:10.1007/3-540-45561-2_32
1657:10.1109/TIM.2018.2836058
1382:, IEEE, pp. 39â43,
1214:Rudolph, GĂŒnter (2001).
578:proposes scatter search.
541:evolutionary programming
299:mathematical programming
267:evolutionary computation
219:evolutionary computation
4286:Parallel metaheuristics
4094:Approximation algorithm
3805:Powell's dog leg method
3757:DavidonâFletcherâPowell
3653:Unconstrained nonlinear
2900:10.1214/aoms/1177729586
2856:10.1145/3067695.3082466
2797:10.1145/3067695.3082473
2719:10.1145/3301326.3301356
1863:10.1145/3415958.3433041
1388:10.1109/MHS.1995.494215
1104:Goldberg, D.E. (1989).
686:Evolutionary algorithms
641:ant colony optimization
617:1989: Moscato proposes
434:curse of dimensionality
374:ant colony optimization
370:evolutionary algorithms
325:Parallel metaheuristics
279:Ant colony optimization
215:ant colony optimization
80:stochastic optimization
68:optimization algorithms
4271:Evolutionary algorithm
3854:
3533:Handbook of Heuristics
3324:. Santa Fe Institute.
3068:10.1093/biomet/57.1.97
2985:10.1093/comjnl/7.4.308
2373:Holland, J.H. (1975).
2294:Procedia Manufacturing
1032:Gad, Ahmed G. (2022).
588:1980: Smith describes
502:1965: Matyas proposes
331:parallel metaheuristic
303:constraint programming
168:
108:no-free-lunch theorems
27:Optimization technique
4044:Criss-cross algorithm
3867:Constrained nonlinear
3852:
3673:Golden-section search
3551:Information Sciences.
2950:"Random optimization"
1994:Hybrid Metaheuristics
1528:Hybrid Metaheuristics
856:10.1145/937503.937505
844:ACM Computing Surveys
517:, which was shown by
255:iterated local search
196:iterated local search
163:
3961:Cutting-plane method
3166:Smith, S.F. (1980).
1747:Moscato, P. (1989).
1138:Talbi, E-G. (2009).
698:evolution strategies
530:Evolution Strategies
528:discovers the first
335:parallel programming
227:evolution strategies
100:computer experiments
56:optimization problem
4291:Simulated annealing
4109:Integer programming
4099:Dynamic programming
3939:Convex optimization
3800:LevenbergâMarquardt
3292:1990JCoPh..90..161D
3258:1990PhLA..146..204M
3197:1983Sci...220..671K
3060:1970Bimka..57...97H
2948:Matyas, J. (1965).
2142:2013ApEn..103..368G
2079:10.1109/4235.585892
1649:2019ITIM...68....2B
1232:10.1109/4235.942534
850:(3). ACM: 268â308.
704:Simulated annealing
694:genetic programming
630:threshold accepting
626:simulated annealing
597:simulated annealing
590:genetic programming
513:and Mead propose a
504:random optimization
456:industrial robots.
366:simulated annealing
312:On the other hand,
263:guided local search
251:simulated annealing
188:simulated annealing
134:simple local search
3971:Subgradient method
3855:
3780:Conjugate gradient
3688:NelderâMead method
2167:. pp. 86â91.
1536:10.1007/11890584_1
1380:Conf. Proc. MHS'95
1286:10.1111/itor.12001
972:Complex Scheduling
709:Workforce modeling
690:genetic algorithms
688:and in particular
681:Swarm intelligence
643:in his PhD thesis.
619:memetic algorithms
446:genetic algorithms
438:analytical methods
355:Swarm intelligence
314:Memetic algorithms
275:Swarm intelligence
169:
42:is a higher-level
4326:
4325:
4309:
4308:
4249:
4248:
4245:
4244:
4208:
4207:
4169:
4168:
4065:
4064:
4061:
4060:
4057:
4056:
3928:
3927:
3924:
3923:
3844:
3843:
3840:
3839:
3818:
3817:
3591:Metaheuristics.jl
3542:978-3-319-07123-7
3529:Resende, Mauricio
3246:Physics Letters A
3191:(4598): 671â680.
3032:978-0-471-26516-0
2865:978-1-4503-4939-0
2806:978-1-4503-4939-0
2728:978-1-4503-6553-6
2686:978-3-540-67353-8
2641:978-3-031-16831-4
2606:978-3-642-27466-4
2563:978-3-319-06507-6
2523:978-3-642-08282-5
2487:978-0-9708275-7-9
2407:Decision Sciences
2388:978-0-262-08213-6
2225:978-3-540-78984-0
2182:978-1-4577-1642-3
2047:978-3-642-23246-6
2012:978-3-540-46384-9
1972:Memetic Computing
1872:978-1-4503-8115-4
1827:978-3-662-43504-5
1787:978-1-4613-6964-6
1545:978-3-540-46384-9
1492:978-3-540-58649-4
1397:978-0-7803-2676-7
1360:978-0-471-57148-3
1149:978-0-470-27858-1
1115:978-0-201-15767-3
1015:978-0-486-40258-1
990:978-3-642-23928-1
919:978-1-84821-497-2
889:978-1-4020-7263-5
786:Natural Computing
666:Meta-optimization
661:Stochastic search
569:genetic algorithm
515:simplex heuristic
426:exhaustive search
399:fitness functions
319:mutation operator
223:genetic algorithm
72:iterative methods
16:(Redirected from
4346:
4255:
4171:
4137:
4114:Branch and bound
4104:Greedy algorithm
4084:
4071:
3991:
3947:
3934:
3875:
3862:
3810:Truncated Newton
3725:Wolfe conditions
3708:
3661:
3648:
3621:
3614:
3607:
3598:
3581:
3572:"Metaheuristics"
3546:
3526:
3508:
3507:
3497:
3477:
3471:
3470:
3464:
3456:
3430:
3407:
3401:
3400:
3394:
3386:
3360:
3340:
3334:
3333:
3317:
3311:
3310:
3275:
3269:
3268:
3241:
3235:
3234:
3208:
3180:
3174:
3173:
3163:
3157:
3156:
3153:10.1108/eb005486
3136:
3130:
3129:
3109:
3103:
3102:
3091:Technical Report
3086:
3080:
3079:
3043:
3037:
3036:
3018:
3012:
3011:
3003:
2997:
2996:
2973:Computer Journal
2968:
2962:
2961:
2945:
2939:
2938:
2937:(10): 1337â1342.
2926:
2920:
2919:
2911:
2905:
2904:
2902:
2884:
2875:
2869:
2868:
2843:
2837:
2836:
2816:
2810:
2809:
2784:
2778:
2777:
2749:
2740:
2739:
2702:
2696:
2695:
2694:
2693:
2660:
2654:
2653:
2617:
2611:
2610:
2582:
2576:
2575:
2539:
2533:
2532:
2531:
2530:
2497:
2491:
2490:
2469:
2463:
2462:
2442:
2433:
2432:
2422:
2402:
2393:
2392:
2380:
2370:
2361:
2360:
2350:
2326:
2320:
2319:
2309:
2285:
2279:
2278:
2268:
2266:10.3390/a6020245
2244:
2238:
2237:
2201:
2195:
2194:
2160:
2154:
2153:
2125:
2119:
2112:
2106:
2105:
2089:
2083:
2082:
2058:
2052:
2051:
2023:
2017:
2016:
2003:10.1007/11890584
1988:
1982:
1981:
1979:
1978:
1968:"Aims and scope"
1964:
1958:
1957:
1955:
1954:
1944:"Aims and scope"
1940:
1934:
1933:
1931:
1930:
1921:. Archived from
1916:
1908:
1902:
1901:
1899:
1898:
1892:Semantic Scholar
1882:
1876:
1875:
1846:
1837:
1836:
1835:
1834:
1801:
1792:
1791:
1763:
1757:
1756:
1744:
1735:
1728:
1719:
1718:
1678:
1669:
1668:
1631:D, Binu (2019).
1628:
1622:
1621:
1605:
1599:
1598:
1588:
1569:"Metaheuristics"
1564:
1555:
1554:
1553:
1552:
1519:
1513:
1508:
1502:
1501:
1500:
1499:
1466:
1460:
1459:
1450:(3): 1155â1173.
1435:
1429:
1428:
1407:
1401:
1400:
1371:
1365:
1364:
1346:
1340:
1339:
1337:
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1321:
1312:
1311:
1305:
1297:
1279:
1255:
1246:
1245:
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1211:
1205:
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1178:
1169:
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1160:
1154:
1153:
1135:
1120:
1119:
1101:
1095:
1094:
1070:
1064:
1063:
1053:
1044:(5): 2531â2561.
1029:
1020:
1019:
1001:
995:
994:
966:
960:
959:
935:
924:
923:
903:
894:
893:
875:
860:
859:
835:
810:
809:
783:
774:
759:
758:
748:
724:
676:Hyper-heuristics
307:machine learning
247:population-based
211:population-based
84:random variables
60:machine learning
52:search algorithm
32:computer science
21:
4354:
4353:
4349:
4348:
4347:
4345:
4344:
4343:
4329:
4328:
4327:
4322:
4305:
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4204:
4165:
4142:
4131:
4124:
4078:
4053:
4017:
3984:
3975:
3952:
3941:
3920:
3894:
3890:Penalty methods
3885:Barrier methods
3869:
3856:
3836:
3832:Newton's method
3814:
3766:
3729:
3697:
3678:Powell's method
3655:
3642:
3625:
3566:
3563:
3543:
3524:
3519:
3516:
3514:Further reading
3511:
3479:
3478:
3474:
3457:
3428:10.1.1.186.6007
3409:
3408:
3404:
3387:
3342:
3341:
3337:
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3206:10.1.1.123.7607
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2584:
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2499:
2498:
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2488:
2471:
2470:
2466:
2444:
2443:
2436:
2420:10.1.1.302.4071
2404:
2403:
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2372:
2371:
2364:
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2287:
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2203:
2202:
2198:
2183:
2162:
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2157:
2127:
2126:
2122:
2113:
2109:
2098:Complex Systems
2091:
2090:
2086:
2060:
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2055:
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2024:
2020:
2013:
1990:
1989:
1985:
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1795:
1788:
1765:
1764:
1760:
1746:
1745:
1738:
1729:
1722:
1680:
1679:
1672:
1630:
1629:
1625:
1607:
1606:
1602:
1566:
1565:
1558:
1550:
1548:
1546:
1521:
1520:
1516:
1509:
1505:
1497:
1495:
1493:
1468:
1467:
1463:
1437:
1436:
1432:
1426:
1409:
1408:
1404:
1398:
1373:
1372:
1368:
1361:
1348:
1347:
1343:
1334:
1332:
1323:
1322:
1315:
1298:
1277:10.1.1.470.3422
1257:
1256:
1249:
1213:
1212:
1208:
1176:
1171:
1170:
1166:
1161:
1157:
1150:
1137:
1136:
1123:
1116:
1103:
1102:
1098:
1072:
1071:
1067:
1031:
1030:
1023:
1016:
1003:
1002:
998:
991:
968:
967:
963:
937:
936:
927:
920:
905:
904:
897:
890:
877:
876:
863:
837:
836:
813:
781:
776:
775:
762:
726:
725:
721:
717:
657:
539:et al. propose
526:Ingo Rechenberg
475:
462:
394:
361:
353:Main articles:
351:
327:
295:
243:
177:
158:
120:
28:
23:
22:
15:
12:
11:
5:
4352:
4350:
4342:
4341:
4339:Metaheuristics
4331:
4330:
4324:
4323:
4321:
4320:
4314:
4311:
4310:
4307:
4306:
4304:
4303:
4298:
4293:
4288:
4283:
4278:
4273:
4267:
4264:
4263:
4260:Metaheuristics
4258:
4251:
4250:
4247:
4246:
4243:
4242:
4240:
4239:
4234:
4232:FordâFulkerson
4229:
4224:
4218:
4216:
4210:
4209:
4206:
4205:
4203:
4202:
4200:FloydâWarshall
4197:
4192:
4191:
4190:
4179:
4177:
4167:
4166:
4164:
4163:
4158:
4153:
4147:
4145:
4134:
4126:
4125:
4123:
4122:
4121:
4120:
4106:
4101:
4096:
4090:
4088:
4080:
4079:
4074:
4067:
4066:
4063:
4062:
4059:
4058:
4055:
4054:
4052:
4051:
4046:
4041:
4036:
4030:
4028:
4019:
4018:
4016:
4015:
4010:
4005:
4003:Affine scaling
3999:
3997:
3995:Interior point
3988:
3977:
3976:
3974:
3973:
3968:
3963:
3957:
3955:
3943:
3942:
3937:
3930:
3929:
3926:
3925:
3922:
3921:
3919:
3918:
3913:
3908:
3902:
3900:
3899:Differentiable
3896:
3895:
3893:
3892:
3887:
3881:
3879:
3871:
3870:
3865:
3858:
3857:
3847:
3845:
3842:
3841:
3838:
3837:
3835:
3834:
3828:
3826:
3820:
3819:
3816:
3815:
3813:
3812:
3807:
3802:
3797:
3792:
3787:
3782:
3776:
3774:
3768:
3767:
3765:
3764:
3759:
3754:
3745:
3739:
3737:
3731:
3730:
3728:
3727:
3722:
3716:
3714:
3705:
3699:
3698:
3696:
3695:
3690:
3685:
3680:
3675:
3669:
3667:
3657:
3656:
3651:
3644:
3643:
3626:
3624:
3623:
3616:
3609:
3601:
3595:
3594:
3588:
3582:
3562:
3561:External links
3559:
3558:
3557:
3547:
3541:
3515:
3512:
3510:
3509:
3495:10.1.1.35.5850
3488:(1): 131â144.
3472:
3421:(1): 121â146.
3402:
3351:(6): 317â321.
3335:
3312:
3286:(1): 161â175,
3270:
3252:(4): 204â208,
3236:
3175:
3158:
3147:(3): 215â228.
3131:
3120:(2): 291â307.
3104:
3081:
3038:
3031:
3013:
2998:
2979:(4): 308â313.
2963:
2940:
2921:
2906:
2893:(3): 400â407.
2870:
2864:
2838:
2827:(4): 658â670.
2811:
2805:
2779:
2760:(3): 527â561.
2754:Soft Computing
2741:
2727:
2697:
2685:
2655:
2640:
2612:
2605:
2577:
2562:
2534:
2522:
2492:
2486:
2464:
2453:(5): 533â549.
2434:
2413:(1): 156â166.
2394:
2387:
2362:
2321:
2280:
2259:(2): 245â277.
2239:
2224:
2196:
2181:
2155:
2130:Applied Energy
2120:
2107:
2084:
2053:
2046:
2018:
2011:
1983:
1959:
1935:
1903:
1877:
1871:
1838:
1826:
1793:
1786:
1758:
1736:
1720:
1670:
1623:
1600:
1556:
1544:
1514:
1503:
1491:
1461:
1430:
1424:
1402:
1396:
1366:
1359:
1341:
1313:
1247:
1226:(4): 410â414.
1206:
1187:(5): 533â549.
1164:
1155:
1148:
1121:
1114:
1096:
1065:
1021:
1014:
996:
989:
961:
925:
918:
895:
888:
861:
811:
792:(2): 239â287.
760:
739:(4): 353â381.
718:
716:
713:
712:
711:
706:
701:
683:
678:
673:
668:
663:
656:
653:
652:
651:
644:
633:
632:metaheuristic.
622:
615:
600:
593:
586:
579:
572:
561:
554:
551:
544:
533:
522:
507:
500:
493:
482:
474:
471:
461:
458:
393:
390:
350:
347:
326:
323:
294:
291:
242:
239:
176:
173:
157:
156:Classification
154:
153:
152:
149:
146:
143:
140:
137:
130:
127:
119:
116:
86:generated. In
26:
24:
18:Metaheuristics
14:
13:
10:
9:
6:
4:
3:
2:
4351:
4340:
4337:
4336:
4334:
4319:
4316:
4315:
4312:
4302:
4299:
4297:
4294:
4292:
4289:
4287:
4284:
4282:
4279:
4277:
4276:Hill climbing
4274:
4272:
4269:
4268:
4265:
4261:
4256:
4252:
4238:
4235:
4233:
4230:
4228:
4225:
4223:
4220:
4219:
4217:
4215:
4214:Network flows
4211:
4201:
4198:
4196:
4193:
4189:
4186:
4185:
4184:
4181:
4180:
4178:
4176:
4175:Shortest path
4172:
4162:
4159:
4157:
4154:
4152:
4149:
4148:
4146:
4144:
4143:spanning tree
4138:
4135:
4133:
4127:
4119:
4115:
4112:
4111:
4110:
4107:
4105:
4102:
4100:
4097:
4095:
4092:
4091:
4089:
4085:
4081:
4077:
4076:Combinatorial
4072:
4068:
4050:
4047:
4045:
4042:
4040:
4037:
4035:
4032:
4031:
4029:
4027:
4024:
4020:
4014:
4011:
4009:
4006:
4004:
4001:
4000:
3998:
3996:
3992:
3989:
3987:
3982:
3978:
3972:
3969:
3967:
3964:
3962:
3959:
3958:
3956:
3954:
3948:
3944:
3940:
3935:
3931:
3917:
3914:
3912:
3909:
3907:
3904:
3903:
3901:
3897:
3891:
3888:
3886:
3883:
3882:
3880:
3876:
3872:
3868:
3863:
3859:
3851:
3833:
3830:
3829:
3827:
3825:
3821:
3811:
3808:
3806:
3803:
3801:
3798:
3796:
3793:
3791:
3788:
3786:
3783:
3781:
3778:
3777:
3775:
3773:
3772:Other methods
3769:
3763:
3760:
3758:
3755:
3753:
3749:
3746:
3744:
3741:
3740:
3738:
3736:
3732:
3726:
3723:
3721:
3718:
3717:
3715:
3713:
3709:
3706:
3704:
3700:
3694:
3691:
3689:
3686:
3684:
3681:
3679:
3676:
3674:
3671:
3670:
3668:
3666:
3662:
3658:
3654:
3649:
3645:
3641:
3637:
3633:
3629:
3622:
3617:
3615:
3610:
3608:
3603:
3602:
3599:
3592:
3589:
3586:
3583:
3579:
3578:
3573:
3569:
3565:
3564:
3560:
3555:
3552:
3548:
3544:
3538:
3534:
3530:
3523:
3518:
3517:
3513:
3505:
3501:
3496:
3491:
3487:
3483:
3476:
3473:
3468:
3462:
3454:
3450:
3446:
3442:
3438:
3434:
3429:
3424:
3420:
3416:
3412:
3406:
3403:
3398:
3392:
3384:
3380:
3376:
3372:
3368:
3364:
3359:
3354:
3350:
3346:
3339:
3336:
3331:
3327:
3323:
3316:
3313:
3309:
3305:
3301:
3297:
3293:
3289:
3285:
3281:
3274:
3271:
3267:
3263:
3259:
3255:
3251:
3247:
3240:
3237:
3232:
3228:
3224:
3220:
3216:
3212:
3207:
3202:
3198:
3194:
3190:
3186:
3179:
3176:
3171:
3170:
3162:
3159:
3154:
3150:
3146:
3142:
3135:
3132:
3127:
3123:
3119:
3115:
3108:
3105:
3100:
3096:
3092:
3085:
3082:
3077:
3073:
3069:
3065:
3061:
3057:
3054:(1): 97â109.
3053:
3049:
3042:
3039:
3034:
3028:
3024:
3017:
3014:
3009:
3002:
2999:
2994:
2990:
2986:
2982:
2978:
2974:
2967:
2964:
2960:(2): 246â253.
2959:
2955:
2951:
2944:
2941:
2936:
2932:
2925:
2922:
2917:
2910:
2907:
2901:
2896:
2892:
2888:
2881:
2874:
2871:
2867:
2861:
2857:
2853:
2849:
2842:
2839:
2834:
2830:
2826:
2822:
2815:
2812:
2808:
2802:
2798:
2794:
2790:
2783:
2780:
2775:
2771:
2767:
2763:
2759:
2755:
2748:
2746:
2742:
2738:
2734:
2730:
2724:
2720:
2716:
2712:
2708:
2701:
2698:
2688:
2682:
2678:
2674:
2670:
2666:
2659:
2656:
2651:
2647:
2643:
2637:
2633:
2629:
2625:
2624:
2616:
2613:
2608:
2602:
2598:
2594:
2590:
2589:
2581:
2578:
2573:
2569:
2565:
2559:
2555:
2551:
2547:
2546:
2538:
2535:
2525:
2519:
2515:
2511:
2507:
2503:
2496:
2493:
2489:
2483:
2479:
2475:
2468:
2465:
2460:
2456:
2452:
2448:
2441:
2439:
2435:
2430:
2426:
2421:
2416:
2412:
2408:
2401:
2399:
2395:
2390:
2384:
2379:
2378:
2369:
2367:
2363:
2358:
2354:
2349:
2344:
2340:
2336:
2335:Procedia CIRP
2332:
2325:
2322:
2317:
2313:
2308:
2303:
2300:: 1177â1184.
2299:
2295:
2291:
2284:
2281:
2276:
2272:
2267:
2262:
2258:
2254:
2250:
2243:
2240:
2235:
2231:
2227:
2221:
2217:
2213:
2209:
2208:
2200:
2197:
2192:
2188:
2184:
2178:
2174:
2170:
2166:
2159:
2156:
2151:
2147:
2143:
2139:
2135:
2131:
2124:
2121:
2117:
2111:
2108:
2103:
2099:
2095:
2088:
2085:
2080:
2076:
2072:
2068:
2064:
2057:
2054:
2049:
2043:
2039:
2035:
2031:
2030:
2022:
2019:
2014:
2008:
2004:
2000:
1996:
1995:
1987:
1984:
1973:
1969:
1963:
1960:
1949:
1945:
1939:
1936:
1925:on 2017-07-09
1924:
1920:
1913:
1907:
1904:
1893:
1889:
1881:
1878:
1874:
1868:
1864:
1860:
1856:
1852:
1845:
1843:
1839:
1829:
1823:
1819:
1815:
1811:
1807:
1800:
1798:
1794:
1789:
1783:
1779:
1775:
1771:
1770:
1762:
1759:
1755:(report 826).
1754:
1750:
1743:
1741:
1737:
1733:
1727:
1725:
1721:
1716:
1712:
1708:
1704:
1700:
1696:
1692:
1688:
1684:
1677:
1675:
1671:
1666:
1662:
1658:
1654:
1650:
1646:
1642:
1638:
1634:
1627:
1624:
1619:
1615:
1611:
1604:
1601:
1596:
1592:
1587:
1582:
1578:
1574:
1570:
1563:
1561:
1557:
1547:
1541:
1537:
1533:
1529:
1525:
1518:
1515:
1512:
1507:
1504:
1494:
1488:
1484:
1480:
1476:
1472:
1465:
1462:
1457:
1453:
1449:
1445:
1441:
1434:
1431:
1427:
1425:9780262720199
1421:
1417:
1413:
1406:
1403:
1399:
1393:
1389:
1385:
1381:
1377:
1370:
1367:
1362:
1356:
1352:
1345:
1342:
1331:
1327:
1320:
1318:
1314:
1309:
1303:
1295:
1291:
1287:
1283:
1278:
1273:
1269:
1265:
1261:
1254:
1252:
1248:
1242:
1237:
1233:
1229:
1225:
1221:
1217:
1210:
1207:
1202:
1198:
1194:
1190:
1186:
1182:
1175:
1168:
1165:
1159:
1156:
1151:
1145:
1141:
1134:
1132:
1130:
1128:
1126:
1122:
1117:
1111:
1107:
1100:
1097:
1092:
1088:
1084:
1080:
1076:
1069:
1066:
1061:
1057:
1052:
1047:
1043:
1039:
1035:
1028:
1026:
1022:
1017:
1011:
1007:
1000:
997:
992:
986:
982:
978:
974:
973:
965:
962:
957:
953:
949:
945:
941:
934:
932:
930:
926:
921:
915:
911:
910:
902:
900:
896:
891:
885:
881:
874:
872:
870:
868:
866:
862:
857:
853:
849:
845:
841:
834:
832:
830:
828:
826:
824:
822:
820:
818:
816:
812:
807:
803:
799:
795:
791:
787:
780:
773:
771:
769:
767:
765:
761:
756:
752:
747:
742:
738:
734:
730:
723:
720:
714:
710:
707:
705:
702:
699:
695:
691:
687:
684:
682:
679:
677:
674:
672:
671:Matheuristics
669:
667:
664:
662:
659:
658:
654:
649:
648:no free lunch
645:
642:
638:
634:
631:
627:
623:
620:
616:
613:
612:metaheuristic
609:
605:
601:
598:
594:
591:
587:
584:
580:
577:
573:
570:
567:proposes the
566:
562:
559:
555:
552:
549:
545:
542:
538:
534:
531:
527:
523:
520:
516:
512:
508:
505:
501:
498:
497:random search
494:
491:
487:
483:
480:
479:
478:
473:Contributions
472:
470:
466:
459:
457:
453:
451:
447:
441:
439:
435:
431:
427:
423:
422:exponentially
419:
415:
411:
407:
402:
400:
391:
389:
386:
381:
379:
375:
371:
367:
360:
356:
348:
346:
342:
340:
336:
332:
324:
322:
320:
315:
310:
308:
304:
300:
292:
290:
288:
284:
280:
276:
272:
268:
264:
260:
256:
252:
248:
240:
238:
236:
232:
228:
224:
220:
216:
212:
207:
205:
201:
197:
193:
189:
184:
182:
181:hill climbing
174:
172:
166:
165:Euler diagram
162:
155:
150:
147:
144:
141:
138:
135:
131:
128:
125:
124:
123:
117:
115:
111:
109:
105:
101:
96:
93:
89:
85:
81:
77:
73:
69:
64:
61:
57:
53:
49:
45:
41:
40:metaheuristic
37:
33:
19:
4281:Local search
4259:
4227:EdmondsâKarp
4183:BellmanâFord
3953:minimization
3785:GaussâNewton
3735:QuasiâNewton
3720:Trust region
3628:Optimization
3577:Scholarpedia
3575:
3550:
3535:. Springer.
3532:
3485:
3481:
3475:
3461:cite journal
3418:
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