Process / pipelineSimulation / optimization
Multi-Objective Particle Swarm Optimization (MOPSO)
Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.
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Sources
- Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279. DOI: 10.1109/TEVC.2004.826067 ↗
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN), Perth, Australia, 4, 1942–1948. DOI: 10.1109/ICNN.1995.488968 ↗
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Referenced by
Agent-based multi-objective optimizationBayesian Particle Swarm OptimizationDeterministic Particle Swarm OptimizationMulti-objective ant colony optimizationMulti-objective genetic algorithmMulti-objective simulated annealingMulti-objective Tabu SearchPolicy Scenario Particle Swarm OptimizationRobust Particle Swarm OptimizationStochastic Particle Swarm Optimization