Võrdle meetodeid

	Sipelgakoloonia optimeerimine	Diferentsiaalne evolutsioon – globaalne stohhastiline optimeerija	Particle Swarm Optimization (PSO)
Valdkond	Optimeerimine	Optimeerimine	Optimeerimine
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1997	1995
Looja	—	Rainer Storn & Kenneth Price	—
Tüüp	Metaheuristic — swarm intelligence	Population-based stochastic metaheuristic	Population-based metaheuristic / swarm intelligence
Algallikas	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.
Rööpnimetused	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	DE algorithm, Diferansiyel Evrim (DE), DE optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Seotud	5	5	6
Kokkuvõte	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.