Methoden vergleichen

	Multi-Objective Particle Swarm Optimization (MOPSO)	Partikelschwarmoptimierung (PSO)
Fachgebiet	Simulation	Optimierung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2004	1995
Urheber	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.	—
Typ	Population-based swarm metaheuristic	Population-based metaheuristic / swarm intelligence
Wegweisende Quelle	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Aliasnamen	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Verwandt	5	6
Zusammenfassung	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.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.