قارن الطرق

	التطور التفاضلي	الخوارزمية الجينية	تحسين السرب الجسيمي (PSO)
المجال	التحسين	التحسين	التحسين
العائلة	Process / pipeline	Process / pipeline	Process / pipeline
سنة النشأة	1997	1975	1995
صاحب الطريقة	Rainer Storn & Kenneth Price	John Henry Holland	—
النوع	Population-based stochastic metaheuristic	Population-based metaheuristic	Population-based metaheuristic / swarm intelligence
المصدر التأسيسي	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
الأسماء البديلة	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
ذات صلة	5	5	6
الملخص	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.	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.	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.