방법 비교

	결정론적 입자 군집 최적화	유전 알고리즘
분야	시뮬레이션	최적화
계열	Process / pipeline	Process / pipeline
기원 연도	1995 (PSO); deterministic formulation circa 2002	1975
창시자	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature	John Henry Holland
유형	Swarm intelligence metaheuristic — deterministic variant	Population-based metaheuristic
원전	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
별칭	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
관련	6	5
요약	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.