Porovnat metody

	Genetický algoritmus	Optimalizace mravenčí kolonií	Diferenciální evoluce	Optimalizace rojem částic (PSO)
Obor	Optimalizace	Optimalizace	Optimalizace	Optimalizace
Rodina	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1975	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1997	1995
Tvůrce	John Henry Holland	—	Rainer Storn & Kenneth Price	—
Typ	Population-based metaheuristic	Metaheuristic — swarm intelligence	Population-based stochastic metaheuristic	Population-based metaheuristic / swarm intelligence
Původní zdroj	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.	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.
Další názvy	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	DE algorithm, Diferansiyel Evrim (DE), DE optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Příbuzné	5	5	5	6
Shrnutí	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.	Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.	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.