Table of metaheuristics

Last updated

This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.

Contents

Categories

The table

NameAbbreviationMain categorySubcategoryYear publishedRef.
Simulated Annealing SATrajectory-based-1983 [1]
Tabu Search TSTrajectory-based-1989 [2]
Genetic Algorithm GAEvolutionary-based-1992 [3]
Evolutionary Algorithm EAEvolutionary-based-1994
Cultural Algorithm CA1994 [4]
Particle Swarm Optimization PSONature-inspiredSwarm-based1995 [5]
Differential EvaluationDEEvolutionary-based-1997 [6]
Local Search LS1997
Variable neighborhood search VNSTrajectory-based-1997 [7]
Guided Local SearchGLSTrajectory-based-1998 [8]
Clonal Selection AlgorithmCSAEvolutionary-based-2000 [9]
Harmony Search HSEvolutionary-based-2001 [10]
Memetic Algorithm MAEvolutionary-based-2002
Iterative Local SearchILSTrajectory-based-2003 [11]
Artificial Bee ColonyABCNature-inspiredBio-inspired2005 [12]
Ant Colony Optimization ACONature-inspiredBio-inspired2006 [13]
Glowworm Swarm Optimization GSONature-inspiredSwarm-based2006 [14]
Shuffled Frog Leaping AlgorithmSFLANature-inspiredBio-inspired2006 [15]
Invasive Weed OptimizationIWONature-inspiredPlant-based2006 [16]
Seeker Optimization AlgorithmSOANature-inspiredHuman-based2006 [17]
Imperialistic Competitive AlgorithmICANature-inspiredHuman-based2007 [18]
Central Force OptimizationCFO2007 [19]
Biogeography Based OptimizationBBONature-inspiredHuman-based2008 [20]
Firefly AlgorithmFANature-inspiredBio-inspired2008 [21]
Intelligent Water DropsIWDNature-inspiredSwarm-based2008 [22]
Monkey AlgorithmMANature-inspiredBio-inspired2008 [23]
Cuckoo SearchCSNature-inspiredBio-inspired2009 [24]
Group Search OptimizerGSONature-inspiredSwarm-based2009 [25]
Key Cutting AlgorithmKCA2009 [26]
Hunting SearchHSNature-inspiredSwarm-based2009 [27]
Chemical Reaction OptimizationCRONature-inspiredPhysics/Chemistry-based2009 [28]
Bat AlgorithmBANature-inspiredBio-inspired2010 [29]
Charged System SearchCSSNature-inspiredPhysics/Chemistry-based2010 [30]
Eagle StrategyESNature-inspired2010
Fireworks AlgorithmFWA2010 [31]
Cuckoo Optimization AlgorithmCOANature-inspiredBio-inspired2011 [32]
Stochastic Diffusion SearchSDS2011
Teaching-Learning-Based OptimizationTLBONature-inspiredHuman-based2011 [33]
Bacterial Colony OptimizationBCO2012 [34]
Fruit Fly OptimizationFFO2012
Krill Herd AlgorithmKHANature-inspiredBio-inspired2012 [35]
Migrating Birds OptimizationMBONature-inspiredSwarm-based2012 [36]
Water Cycle AlgorithmWCA2012
Backtracking Search AlgorithmBSAEvolutionary-based-2013 [37]
Black Hole AlgorithmBHNature-inspiredPhysics/Chemistry-based2013 [38]
Dolphin EcholocationDENature-inspiredBio-inspired2013 [39]
Animal Migration OptimizationAMONature-inspiredSwarm-based2013 [40]
Keshtel AlgorithmKANature-inspired2014 [41]
SDA Optimization AlgorithmSDANature-inspiredBio-inspired2014 [42]
Artificial Root Foraging AlgorithmARFANature-inspiredPlant-based2014 [43]
Bumble Bees Mating OptimizationBBMO2014
Chicken Swarm OptimizationCSONature-inspiredBio-inspired2014 [44]
Colliding Bodies OptimizationCBO2014 [45]
Coral Reefs Optimization AlgorithmCROA2014
Flower Pollination AlgorithmFPANature-inspiredPlant-based2014 [46]
Radial Movement OptimizationRMONature-inspiredSwarm-based2014 [47]
Spider Monkey OptimizationSMONature-inspiredBio-inspired2014 [48]
Soccer League CompetitionSLCNature-inspiredHuman-based2014 [49]
Artificial Algae AlgorithmAAA2015 [50]
Adaptive Dimensional SearchADS2015
Alienated Ant AlgorithmAAA2015
Artificial Fish Swarm AlgorithmAFSANature-inspired2015
Bottlenose Dolphin OptimizationBDONature-inspired2015 [51]
Cricket AlgorithmCA2015 [52]
Elephant Search AlgorithmESANature-inspiredBio-inspired2015 [53]
Grey Wolf OptimizerGWONature-inspiredBio-inspired2015 [54]
Jaguar AlgorithmJANature-inspiredBio-inspired2015 [55]
Locust Swarm AlgorithmLSANature-inspiredSwarm-based2015 [56]
Moth-Flame OptimizationMFONature-inspiredBio-inspired2015 [57]
Stochastic Fractal SearchSFFEvolutionary-based-2015 [58]
Vortex Search AlgorithmVSANature-inspiredPhysics/Chemistry-based2015 [59]
Water Wave OptimizationWWANature-inspiredPhysics/Chemistry-based2015 [60]
Ant Lion OptimizerALONature-inspiredBio-inspired2015 [61]
African Buffalo OptimizationABONature-inspiredSwarm-based2015 [62]
Lightning Search AlgorithmLSANature-inspiredPhysics/Chemistry-based2015 [63]
Across Neighborhood SearchANSEvolutionary-based-2016 [64]
Crow Search AlgorithmCSANature-inspiredBio-inspired2016 [65]
Electromagnetic Field OptimizationEFONature-inspiredPhysics/Chemistry-based2016 [66]
Joint Operations AlgorithmJOANature-inspiredSwarm-based2016 [67]
Lion Optimization AlgorithmLOANature-inspiredBio-inspired2016 [68]
Sine Cosine AlgorithmSCANature-inspiredPhysics/Chemistry-based2016 [69]
Virus Colony SearchVCSNature-inspiredBio-inspired2016 [70]
Whale Optimization AlgorithmWOANature-inspiredBio-inspired2016 [71]
Red Deer AlgorithmRDANature-inspiredBio-inspired2016 [72]
Phototropic Optimization AlgorithmPOANature-inspiredPlant-based2018 [73]
Coyote Optimization AlgorithmCOANature-inspiredSwarm-based2018 [74]
Owl Search AlgorithmOSANature-inspiredBio-inspired2018 [75]
Squirrel Search AlgorithmSSANature-inspiredBio-inspired2018 [76]
Social Engineering OptimizerSEONature-inspiredHuman-based2018 [77]
Emperor Penguin OptimizerEPONature-inspiredBio-inspired2018 [78]
Socio Evolution and Learning OptimizationSELONature-inspiredHuman-based2018 [79]
Future Search AlgorithmFSANature-inspiredHuman-based2019 [80]
Emperor Penguins ColonyEPCNature-inspiredSwarm-based2019 [81]
Thermal Exchange OptimizationTEONature-inspiredPhysics/Chemistry-based2019 [82]
Harris Hawks OptimizationHHONature-inspiredBio-inspired2019 [83]
Political OptimizerPONature-inspiredHuman-based2020 [84]
Heap-Based OptimizerHBONature-inspiredHuman-based2020 [85]
Color Harmony AlgorithmCHAArt-inspiredColor-based2020 [86]
Stochastic Paint OptimizerSPOArt-inspiredColor-based2020 [87]
Mayfly Optimization AlgorithmMOANature-inspiredBio-inspired2020 [88]
Giza Pyramids ConstructionGPCAncient-inspired-2020 [89]
Fire Hawk OptimizerFHONature-inspiredBio-inspired2022 [90]
Flying Fox Optimization AlgorithmFFONature-inspiredBio-inspired2023 [91]
Waterwheel Plant AlgorithmWWPANature-inspiredPlant-based2023 [92]
Energy Valley OptimizerEVONature-inspiredPhysics/Chemistry-based2023 [93]
Special Forces AlgorithmSFANature-inspiredSwarm-based2023 [94]
Squid Game OptimizerSGONature-inspiredHuman-based2023 [95]
Snow Ablation OptimizerSAONature-inspiredPhysics/Chemistry-based2023 [96]
Spider Wasp OptimizationSWONature-inspiredBio-inspired2023 [97]
Dujiangyan Irrigation SystemDISOAncient-inspired-2023 [98]
Great Wall Construction AlgorithmGWCAAncient-inspired-2023 [99]
Puma OptimizerPONature-inspiredBio-inspired2024 [100]
Walrus OptimizerWONature-inspiredBio-inspired2024 [101]

Related Research Articles

<span class="mw-page-title-main">Genetic algorithm</span> Competitive algorithm for searching a problem space

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.

<span class="mw-page-title-main">Evolutionary computation</span> Trial and error problem solvers with a metaheuristic or stochastic optimization character

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<span class="mw-page-title-main">Particle swarm optimization</span> Iterative simulation method

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<span class="mw-page-title-main">Ant colony optimization algorithms</span> Optimization algorithm

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<span class="mw-page-title-main">Swarm intelligence</span> Collective behavior of decentralized, self-organized systems

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A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence.

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In computer science, imperialist competitive algorithms are a type of computational method used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does not need the gradient of the function in its optimization process. From a specific point of view, ICA can be thought of as the social counterpart of genetic algorithms (GAs). ICA is the mathematical model and the computer simulation of human social evolution, while GAs are based on the biological evolution of species.

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<span class="mw-page-title-main">Maurice Clerc (mathematician)</span> French mathematician

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References

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