方法对比

	遗传算法	差分进化	粒子群优化 (PSO)
领域	优化	优化	优化
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1975	1997	1995
提出者	John Henry Holland	Rainer Storn & Kenneth Price	—
类型	Population-based metaheuristic	Population-based stochastic metaheuristic	Population-based metaheuristic / swarm intelligence
开创性文献	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
别名	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	DE algorithm, Diferansiyel Evrim (DE), DE optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
相关	5	5	6
摘要	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.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.