পদ্ধতির তুলনা করুন

	NSGA-II	ডিফারেনশিয়াল ইভোলিউশন	জেনেটিক অ্যালগরিদম
ক্ষেত্র	অনুকূলকরণ	অনুকূলকরণ	অনুকূলকরণ
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2002	1997	1975
প্রবর্তক	—	Rainer Storn & Kenneth Price	John Henry Holland
ধরন	Evolutionary multi-objective optimisation algorithm	Population-based stochastic metaheuristic	Population-based metaheuristic
মৌলিক উৎস	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.	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.
অপর নাম	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
সম্পর্কিত	4	5	5
সারসংক্ষেপ	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.	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.