方法对比

	粒子群优化 (PSO)	差分进化
领域	优化	优化
方法族	Process / pipeline	Process / pipeline
起源年份	1995	1997
提出者	—	Rainer Storn & Kenneth Price
类型	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic
开创性文献	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	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.
别名	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	DE algorithm, Diferansiyel Evrim (DE), DE optimization
相关	6	5
摘要	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.	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.