Comparar métodos

	Evolución Estratégica (CMA-ES)	Optimización por Enjambre de Partículas (PSO)
Campo	Optimización	Optimización
Familia	Process / pipeline	Process / pipeline
Año de origen	2001	1995
Autor original	Nikolaus Hansen & Andreas Ostermeier	—
Tipo	Derivative-free continuous black-box optimizer	Population-based metaheuristic / swarm intelligence
Fuente seminal	Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	CMA-ES, Evolution Strategy, Evrimsel Strateji (CMA-ES), self-adapting evolution strategy	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	5	6
Resumen	CMA-ES, short for Covariance Matrix Adaptation Evolution Strategy, is a modern derivative-free optimizer for continuous black-box functions introduced by Hansen and Ostermeier in 2001. It maintains a population of candidate solutions drawn from a multivariate normal distribution and iteratively updates the distribution's mean, step size, and full covariance matrix to steer the search toward better regions of the parameter space. It has become the de-facto standard for continuous black-box optimization and is widely used in neural architecture search and reinforcement-learning policy optimization.	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.