Table of metaheuristics

Last updated

This is a chronological table of metaheuristic algorithms that only contains fundamental 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]
Giza Pyramids ConstructionGPCAncient-inspired-2020 [88]
Mayfly Optimization AlgorithmMOANature-inspiredBio-inspired2020 [89]
Fire Hawk OptimizerFHONature-inspiredBio-inspired2022 [90]
Flying Fox Optimization AlgorithmFFONature-inspiredBio-inspired2023 [91]

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

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

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

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.

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

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic 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. 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.

In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search space and problem space. In the simplest case, the search space corresponds to the problem space. The choice of problem representation is tied to the choice of genetic operators, both of which have a decisive effect on the efficiency of the optimization. Genetic representation can encode appearance, behavior, physical qualities of individuals. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation.

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.

Search-based software engineering (SBSE) applies metaheuristic search techniques such as genetic algorithms, simulated annealing and tabu search to software engineering problems. Many activities in software engineering can be stated as optimization problems. Optimization techniques of operations research such as linear programming or dynamic programming are often impractical for large scale software engineering problems because of their computational complexity or their assumptions on the problem structure. Researchers and practitioners use metaheuristic search techniques, which impose little assumptions on the problem structure, to find near-optimal or "good-enough" solutions.

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.

<span class="mw-page-title-main">Fred W. Glover</span> American computer scientist

Fred Glover is Chief Scientific Officer of Entanglement, Inc., USA, in charge of algorithmic design and strategic planning for applications of combinatorial optimization in quantum computing. He also holds the title of Distinguished University Professor, Emeritus, at the University of Colorado, Boulder, associated with the College of Engineering and Applied Science and the Leeds School of Business. He is known for his innovations in the area of metaheuristics including the computer-based optimization methodology of Tabu search an adaptive memory programming algorithm for mathematical optimization, and the associated evolutionary Scatter Search and Path Relinking algorithms.

Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems.

<span class="mw-page-title-main">Meta-optimization</span>

In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm.

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.

The brain storm optimization algorithm is a heuristic algorithm that focuses on solving multi-modal problems, such as radio antennas design worked on by Yahya Rahmat-Samii, inspired by the brainstorming process, proposed by Dr. Yuhui Shi.

Lion algorithm (LA) is one among the bio-inspired (or) nature-inspired optimization algorithms (or) that are mainly based on meta-heuristic principles. It was first introduced by B. R. Rajakumar in 2012 in the name, Lion’s Algorithm.. It was further extended in 2014 to solve the system identification problem. This version was referred as LA, which has been applied by many researchers for their optimization problems.

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

Maurice Clerc is a French mathematician.

A large-scale capacitated arc routing problem (LSCARP) is a variant of the capacitated arc routing problem that covers 300 or more edges to model complex arc routing problems at large scales.

References

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