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In computer science and mathematical optimization, a metaheuristic is a higher-level 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.
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.
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.
Variable neighborhood search (VNS), [1] proposed by Mladenović & Hansen in 1997, [2] is a metaheuristic method for solving a set of combinatorial optimization and global optimization problems.
The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search. [1]
Evolutionary computation is a sub-field of the metaheuristic methods. Memetic algorithm (MA), often called hybrid genetic algorithm among others, is a population-based method in which solutions are also subject to local improvement phases.
A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang [4] where a demo program in MATLAB/GNU Octave is available, while a comprehensive review is carried out by Parpinelli and Lopes. [5] A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency. [6]
In computer science and operations research, a memetic algorithm (MA) is an extension of an evolutionary algorithm (EA) that aims to accelerate the evolutionary search for the optimum. An EA is a metaheuristic that reproduces the basic principles of biological evolution as a computer algorithm in order to solve challenging optimization or ...