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Particle swarm optimization

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 q

EU/ME, the metaheuristics community

EWG EU/ME, the EURO Working Group on Metaheuristics, formerly referred to as EU/ME – the metaheuristics community, is a working group the main purpose of which is to provide a platform for communicati

Metaheuristic

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provid

Local search (optimization)

In computer science, local search is a heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximi

Minimum Population Search

In evolutionary computation, Minimum Population Search (MPS) is a computational method that optimizes a problem by iteratively trying to improve a set of candidate solutions with regard to a given mea

Paradiseo

ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics. It uses EO, a template-based, ANSI-C++ compliant computation library. ParadisEO is portable acros

Rider optimization algorithm

The rider optimization algorithm (ROA) is devised based on a novel computing method, namely fictional computing that undergoes series of process to solve the issues of optimizations using imaginary fa

Tabu search

Tabu search is a metaheuristic search method employing local search methods used for mathematical optimization. It was created by Fred W. Glover in 1986 and formalized in 1989. Local (neighborhood) se

Table of metaheuristics

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.

Simulated annealing

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search

Multi-swarm optimization

Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is

Random search

Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized, and RS can hence be used on functions that are not continuous or diffe

Hill climbing

In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a prob

Late acceptance hill climbing

Late acceptance hill climbing, created by Yuri Bykov in 2008 is a metaheuristic search method employing local search methods used for mathematical optimization.

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