قارن الطرق

	خوارزمية التصنيف غير المهيمن ثنائية الترتيب الثانية (NSGA-II)	الخوارزمية الجينية	تحسين السرب الجسيمي (PSO)
المجال	التحسين	التحسين	التحسين
العائلة	Process / pipeline	Process / pipeline	Process / pipeline
سنة النشأة	2002	1975	1995
صاحب الطريقة	—	John Henry Holland	—
النوع	Evolutionary multi-objective optimisation algorithm	Population-based metaheuristic	Population-based metaheuristic / swarm intelligence
المصدر التأسيسي	Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.	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	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
ذات صلة	4	5	6
الملخص	NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.	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.