Metaheuristiken

48 Methoden in dieser Familie.

Ausgewählt

Agentenbasierte Ameisenkolonie-OptimierungAgent-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 i Agentenbasierte 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 a Agent-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, Ameisenkolonie-Optimierung – Schwarmbasierte kombinatorische OptimierungAnt Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulatin Künstliche-Bienenkolonie-Optimierung (ABC)Artificial 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

Leseweg

Die meistzitierten grundlegenden Methoden dieses Themas, in der Reihenfolge ihrer Entwicklung — ein Ausgangspunkt, wenn Sie hier neu sind.

Genetischer Algorithmus1975von John Henry Holland
Simulated Annealing1983
Multi-Objective Genetic Algorithm (MOGA)1984von Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Tabu Search1989von Fred Glover
Ameisenkolonie-Optimierung – Schwarmbasierte kombinatorische Optimierung1992 (foundational thesis); 1997 (Ant Colony System formalization)
Partikelschwarmoptimierung (PSO)1995
Differential Evolution1997von Rainer Storn & Kenneth Price
Multi-Objective Particle Swarm Optimization (MOPSO)2004von Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.

alle Methoden in diesem Regal ↓

Alle Methoden 48

Agentenbasierte Ameisenkolonie-Optimierung Agent-Based Genetic Algorithm Agentenbasierte NSGA-II Agent-Based Tabu Search Ameisenkolonie-Optimierung – Schwarmbasierte kombinatorische Optimierung Künstliche-Bienenkolonie-Optimierung (ABC)Fledermaus-Algorithmus Bayesian Ant Colony Optimization Bayesscher Genetischer Algorithmus Bayesian NSGA-II Bayesian Particle Swarm Optimization Bayesian Simulated Annealing Bayesian Tabu Search Kuckuckssuche – Lévy-Flug Metaheuristik Deterministischer Genetischer Algorithmus Deterministic Particle Swarm Optimization Deterministisches Simuliertes Abkühlen Differential Evolution Firefly Algorithm Genetischer Algorithmus Grey Wolf Optimizer Harmony Search Hyper-Heuristiken Matheuristics Memetischer Algorithmus Multi-Objective Ant Colony Optimization (MOACO)Multi-Objective Genetic Algorithm (MOGA)Multi-Objective Particle Swarm Optimization (MOPSO)Multi-Objective Simulated Annealing (MOSA)Multi-objective Tabu Search (MOTS)NSGA-II NSGA-III Partikelschwarmoptimierung (PSO)Policy Scenario Genetic Algorithm Policy Scenario Particle Swarm Optimization Robuste Ameisenkolonie-Optimierung Robuster Genetischer Algorithmus Robust NSGA-II Robuste Partikelschwarmoptimierung Robust Simulated Annealing Robust Tabu Search Simheuristics Simulated Annealing Stochastischer Genetischer Algorithmus Stochastic NSGA-II Stochastische Partikelschwarmoptimierung Stochastische Tabu-Suche – Randomisierte Metaheuristik mit Gedächtnis Tabu Search