Bandingkan metode

	Optimasi Kawanan Partikel Deterministik	Optimasi Kawanan Partikel Multi-Objektif (MOPSO)
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1995 (PSO); deterministic formulation circa 2002	2004
Pencetus	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Tipe	Swarm intelligence metaheuristic — deterministic variant	Population-based swarm metaheuristic
Sumber perintis	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.	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.
Alias	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Terkait	6	5
Ringkasan	Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.	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.