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This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms.
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.
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.
1988: First conference on genetic algorithms is organized at the University of Illinois at Urbana-Champaign. 1988: Koza registers his first patent on genetic programming. [20] [21] 1989: Goldberg publishes a well known book on genetic algorithms. [22] 1989: Evolver, the first optimization software using the genetic algorithm.
This list may not reflect recent changes. Bio-inspired computing; List of metaphor-based metaheuristics; Spiral optimization algorithm; Swarm intelligence; A.
We first give in Algorithm 3 the steps of the neighborhood change function which will be used later. Function NeighborhoodChange() compares the new value f(x') with the incumbent value f(x) obtained in the neighborhood k (line 1). If an improvement is obtained, k is returned to its initial value and the new incumbent updated (line 2).
Parallel metaheuristic is a class of techniques that are capable of reducing both the numerical effort [clarification needed] and the run time of a metaheuristic. To this end, concepts and technologies from the field of parallelism in computer science are used to enhance and even completely modify the behavior of existing metaheuristics.
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]