Comparar métodos

	Otimização Determinística por Enxame de Partículas	Otimização por Enxame de Partículas (PSO)
Área	Simulação	Otimização
Família	Process / pipeline	Process / pipeline
Ano de origem	1995 (PSO); deterministic formulation circa 2002	1995
Autor original	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature	—
Tipo	Swarm intelligence metaheuristic — deterministic variant	Population-based metaheuristic / swarm intelligence
Fonte seminal	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Outros nomes	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	6	6
Resumo	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.	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.