Metaheuristic

Source: Wikipedia, the free encyclopedia.

In

procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.[1][2] 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 being solved and so may be usable for a variety of problems.[3]

Compared to

feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics.[3] As such, they are useful approaches for optimization problems.[2] Several books and survey papers have been published on the subject.[2][3][4][5][6] Literature review on metaheuristic optimization,[7] suggested that it was Fred Glover who coined the word metaheuristics.[8]

Most literature on metaheuristics is experimental in nature, describing empirical results based on

convergence and the possibility of finding the global optimum.[3] Many metaheuristic methods have been published with claims of novelty and practical efficacy. While the field also features high-quality research, many of the publications have been of poor quality; flaws include vagueness, lack of conceptual elaboration, poor experiments, and ignorance of previous literature.[9]

Properties

These are properties that characterize most metaheuristics:[3]

  • Metaheuristics are strategies that guide the search process.
  • The goal is to efficiently explore the search space in order to find near–optimal solutions.
  • Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.
  • Metaheuristic algorithms are approximate and usually non-deterministic.
  • Metaheuristics are not problem-specific.

Classification

Euler diagram of the different classifications of metaheuristics.[10]

There are a wide variety of metaheuristics[2] and a number of properties with respect to which to classify them.[3]

Local search vs. global search

One approach is to characterize the type of search strategy.[3] One type of search strategy is an improvement on simple local search algorithms. A well known local search algorithm is the hill climbing method which is used to find local optimums. However, hill climbing does not guarantee finding global optimum solutions.

Many metaheuristic ideas were proposed to improve local search heuristic in order to find better solutions. Such metaheuristics include

variable neighborhood search, and GRASP.[3]
These metaheuristics can both be classified as local search-based or global search metaheuristics.

Other global search metaheuristic that are not local search-based are usually

ant colony optimization, evolutionary computation such as genetic algorithm or evolution strategies, particle swarm optimization, rider optimization algorithm[11] and bacterial foraging algorithm.[12]

Single-solution vs. population-based

Another classification dimension is single solution vs

Ant colony optimization,[13] particle swarm optimization,[6] social cognitive optimization and bacterial foraging algorithm[12]
are examples of this category.

Hybridization and memetic algorithms

A hybrid metaheuristic is one that combines a metaheuristic with other optimization approaches, such as algorithms from

mathematical programming, constraint programming, and machine learning
. Both components of a hybrid metaheuristic may run concurrently and exchange information to guide the search.

On the other hand, Memetic algorithms[14] 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 mutation operator in evolutionary algorithms.

Parallel metaheuristics

A

parallel programming to run multiple metaheuristic searches in parallel; these may range from simple distributed
schemes to concurrent search runs that interact to improve the overall solution.

Nature-inspired and metaphor-based metaheuristics

Main articles:
List of metaphor-inspired metaheuristics

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 systems to deal with complex computational problems. Such metaheuristics include

attract criticism in the research community for hiding their lack of novelty behind an elaborate metaphor.[9]

Applications

Metaheuristics are used for all types of optimization problems, ranging from

analytical methods
.

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 possible time.

genetic algorithms by Holland et al.,[23] scatter search[24] and tabu search[25]
by Glover.

Another large field of application are optimization tasks in continuous or mixed-integer search spaces. This includes, e.g., design optimization[26][27][28] or various engineering tasks.[29][30][31] An example of the mixture of combinatorial and continuous optimization is the planning of favourable motion paths for industrial robots.[32][33]

Metaheuristic Optimization Frameworks

A MOF can be defined as ‘‘a 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), which are necessary to solve a particular problem instance using techniques provided’’.[34]

There are many candidate optimization tools which can be considered as a MOF of varying feature: Comet, EvA2, evolvica, Evolutionary::Algorithm, GAPlayground, jaga, JCLEC, JGAP, jMetal, n-genes, Open Beagle, Opt4j, ParadisEO/EO, Pisa, Watchmaker, FOM, Hypercube, HotFrame, Templar, EasyLocal, iOpt, OptQuest, JDEAL, Optimization Algorithm Toolkit, HeuristicLab, MAFRA, Localizer, GALIB, DREAM, Discropt, MALLBA, MAGMA, Metaheuristics.jl, UOF[34] and OptaPlanner.

Contributions

Many different metaheuristics are in existence and new variants are continually being proposed. Some of the most significant contributions to the field are:

See also

References

  1. ^ R. Balamurugan; A.M. Natarajan; K. Premalatha (2015). "Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data". Applied Artificial Intelligence. 29 (4): 353–381.
    S2CID 44624424
    .
  2. ^
    S2CID 9141490
    .
  3. ^ a b c d e f g h i j Blum, C.; Roli, A. (2003). "Metaheuristics in combinatorial optimization: Overview and conceptual comparison". 35 (3). ACM Computing Surveys: 268–308. {{cite journal}}: Cite journal requires |journal= (help)
  4. ^ Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers.
    ISBN 978-0-201-15767-3
    .
  5. ^ Glover, F.; Kochenberger, G.A. (2003). Handbook of metaheuristics. Vol. 57. Springer, International Series in Operations Research & Management Science.
    ISBN 978-1-4020-7263-5
    .
  6. ^ a b c d e Talbi, E-G. (2009). Metaheuristics: from design to implementation. Wiley.
    ISBN 978-0-470-27858-1
    .
  7. ^ X. S. Yang, Metaheuristic optimization, Scholarpedia, 6(8):11472 (2011).
  8. ^ Glover F., (1986). Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 13, 533–549 (1986).
  9. ^
    S2CID 14042315. Archived from the original
    (PDF) on 2013-11-02.
  10. ^ Classification of metaheuristics
  11. S2CID 54459927
    .
  12. ^
    S2CID 257990456
    .
  13. ^ a b M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, 1992.
  14. ^ a b Moscato, P. (1989). "On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms". Caltech Concurrent Computation Program (report 826).
  15. ^ Tomoiagă B, Chindriş M, Sumper A, Sudria-Andreu A, Villafafila-Robles R. Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II. Energies. 2013; 6(3):1439–1455.
  16. doi:10.1016/j.apenergy.2012.09.059
    .
  17. S2CID 698459
    .
  18. ISBN 978-1-84821-497-2
    .
  19. S2CID 42238720
    .
  20. ISSN 1999-4893
    .
  21. S2CID 213710393
    .
  22. S2CID 164850023
    .
  23. ^ a b Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
    ISBN 978-0-262-08213-6
    .
  24. ^ a b Glover, Fred (1977). "Heuristics for Integer programming Using Surrogate Constraints". Decision Sciences. 8 (1): 156–166.
    doi:10.1111/j.1540-5915.1977.tb01074.x
    .
  25. ^ a b Glover, F. (1986). "Future Paths for Integer Programming and Links to Artificial Intelligence". Computers and Operations Research. 13 (5): 533–549.
    doi:10.1016/0305-0548(86)90048-1
    .
  26. doi:10.1016/j.eswa.2021.115351
    .
  27. ISBN 978-0-9708275-7-9
  28. ISBN 978-3-642-08282-5
    , retrieved 2023-07-17
  29. S2CID 40197553
    .
  30. ISBN 978-3-642-27466-4
    .
  31. S2CID 254222401
    .
  32. ISBN 978-3-540-67353-8
    , retrieved 2023-07-17
  33. S2CID 77394395
  34. ^
    S2CID 1497912
    .
  35. ^ Robbins, H.; Monro, S. (1951). "A Stochastic Approximation Method" (PDF). Annals of Mathematical Statistics. 22 (3): 400–407.
    doi:10.1214/aoms/1177729586
    .
  36. ^ Barricelli, N.A. (1954). "Esempi numerici di processi di evoluzione". Methodos: 45–68.
  37. ^ Rastrigin, L.A. (1963). "The convergence of the random search method in the extremal control of a many parameter system". Automation and Remote Control. 24 (10): 1337–1342.
  38. ^ Matyas, J. (1965). "Random optimization". Automation and Remote Control. 26 (2): 246–253.
  39. ^ Nelder, J.A.; Mead, R. (1965). "A simplex method for function minimization". Computer Journal. 7 (4): 308–313.
    S2CID 2208295
    .
  40. ^ Rechenberg, Ingo (1965). "Cybernetic Solution Path of an Experimental Problem". Royal Aircraft Establishment, Library Translation.
  41. ^ Fogel, L.; Owens, A.J.; Walsh, M.J. (1966). Artificial Intelligence through Simulated Evolution. Wiley.
    ISBN 978-0-471-26516-0
    .
  42. ^ Hastings, W.K. (1970). "Monte Carlo Sampling Methods Using Markov Chains and Their Applications". Biometrika. 57 (1): 97–109.
    S2CID 21204149
    .
  43. ^ Cavicchio, D.J. (1970). "Adaptive search using simulated evolution". Technical Report. University of Michigan, Computer and Communication Sciences Department.
    hdl:2027.42/4042
    .
  44. ^ Kernighan, B.W.; Lin, S. (1970). "An efficient heuristic procedure for partitioning graphs". Bell System Technical Journal. 49 (2): 291–307.
    doi:10.1002/j.1538-7305.1970.tb01770.x
    .
  45. ^ Mercer, R.E.; Sampson, J.R. (1978). "Adaptive search using a reproductive metaplan". Kybernetes. 7 (3): 215–228.
    doi:10.1108/eb005486
    .
  46. ^ Smith, S.F. (1980). A Learning System Based on Genetic Adaptive Algorithms (PhD Thesis). University of Pittsburgh.
  47. ^ Kirkpatrick, S.; Gelatt Jr., C.D.; Vecchi, M.P. (1983). "Optimization by Simulated Annealing". Science. 220 (4598): 671–680.
    S2CID 205939
    .
  48. doi:10.1016/0375-9601(90)90166-L
  49. ISSN 0021-9991
  50. ^ Wolpert, D.H.; Macready, W.G. (1995). "No free lunch theorems for search". Technical Report SFI-TR-95-02-010. Santa Fe Institute.
    S2CID 12890367
    .
  51. S2CID 147624.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  52. S2CID 1989533.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  53. doi:10.1016/s0304-3975(02)00094-4
    .

Further reading

External links

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