Bandingkan kaedah

	Algoritma Genetik	NSGA-II	Particle Swarm Optimization (PSO)
Bidang	Pengoptimuman	Pengoptimuman	Pengoptimuman
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1975	2002	1995
Pengasas	John Henry Holland	—	—
Jenis	Population-based metaheuristic	Evolutionary multi-objective optimisation algorithm	Population-based metaheuristic / swarm intelligence
Sumber perintis	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Berkaitan	5	4	6
Ringkasan	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.	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.	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.