Comparar métodos

	Algoritmo Genético	Evolução Diferencial	Otimização por Enxame de Partículas (PSO)
Área	Otimização	Otimização	Otimização
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1975	1997	1995
Autor original	John Henry Holland	Rainer Storn & Kenneth Price	—
Tipo	Population-based metaheuristic	Population-based stochastic metaheuristic	Population-based metaheuristic / swarm intelligence
Fonte seminal	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Outros nomes	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	DE algorithm, Diferansiyel Evrim (DE), DE optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	5	5	6
Resumo	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.	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.	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.