Linganisha mbinu

	Multi-Objective Particle Swarm Optimization (MOPSO)	Uboreshaji wa Malengo Mengi
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2004	1896 (concept); 1989–2002 (evolutionary algorithms era)
Mwanzilishi	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Aina	Population-based swarm metaheuristic	Optimization framework
Chanzo asilia	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.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Majina mbadala	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Zinazohusiana	5	3
Muhtasari	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.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.