Search results
Results From The WOW.Com Content Network
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.
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.
Main page; Contents; Current events; Random article; About Wikipedia; Contact us
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 ...
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.
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).
Tabu search is often benchmarked against other metaheuristic methods — such as simulated annealing, genetic algorithms, ant colony optimization algorithms, reactive search optimization, guided local search, or greedy randomized adaptive search. In addition, tabu search is sometimes combined with other metaheuristics to create hybrid methods.