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Metaheuristik

48 metoder i denna familj.

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Agentbaserad myrkolonioptimeringAgent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on Agent-based genetic algorithmAn Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it iAgent-Based NSGA-IIAgent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running aAgent-Based Tabu SearchAgent-Based Tabu Search (ABTS) embeds the tabu search metaheuristic inside a multi-agent framework where autonomous agents each run independent or cooperating tabu search threads, Myrsamhällesoptimering – svärmbaserad kombinatorisk optimeringAnt Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulatinArtificiell bi-koloni (ABC) optimeringArtificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a hone

Läsväg

Det här ämnets mest refererade grundläggande metoder, i den ordning de utvecklades — en bra startpunkt om du är ny här.

  1. Genetisk algoritm1975av John Henry Holland
  2. Simulated Annealing – Probabilistisk Optimering1983
  3. Multiobjektiv genetisk algoritm (MOGA)1984av Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
  4. Tabu Search1989av Fred Glover
  5. Myrsamhällesoptimering – svärmbaserad kombinatorisk optimering1992 (foundational thesis); 1997 (Ant Colony System formalization)
  6. Partikelsvärmsoptimering (PSO)1995
  7. Differential Evolution1997av Rainer Storn & Kenneth Price
  8. Multi-Objective Particle Swarm Optimization (MOPSO)2004av Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
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Alla metoder 48

Agentbaserad myrkolonioptimeringAgent-based genetic algorithmAgent-Based NSGA-IIAgent-Based Tabu SearchMyrsamhällesoptimering – svärmbaserad kombinatorisk optimeringArtificiell bi-koloni (ABC) optimeringBat AlgorithmBayesian Ant Colony OptimizationBayesiansk Genetisk AlgoritmBayesian NSGA-IIBayesiansk partikelsvärmoptimeringBayesian Simulated AnnealingBayesian Tabu SearchCuckoo Search – Lévy Flight-metaheuristikDeterministisk Genetisk AlgoritmDeterministisk Particle Swarm OptimizationDeterministisk simulerad glödgningDifferential EvolutionEldflugealgoritmenGenetisk algoritmGrey Wolf OptimizerHarmony SearchHyperheuristikerMatheuristics: Hybridisering av matematisk programmering och metaheuristikMemetisk algoritmMulti-Objective Ant Colony Optimization (MOACO)Multiobjektiv genetisk algoritm (MOGA)Multi-Objective Particle Swarm Optimization (MOPSO)Multi-Objective Simulated Annealing (MOSA)Multi-objective Tabu Search (MOTS)NSGA-IINSGA-IIIPartikelsvärmsoptimering (PSO)Policy Scenario Genetic AlgorithmPolicy Scenario Particle Swarm OptimizationRobust Ant Colony OptimizationRobust Genetisk AlgoritmRobust NSGA-IIRobust PartikelsvärmoptimeringRobust Simulated AnnealingRobust Tabu SearchSimheuristicsSimulated Annealing – Probabilistisk OptimeringStokastisk genetisk algoritmStochastic NSGA-IIStokastisk partikelsvärmoptimeringStokastisk TabusökningTabu Search