手法を比較

	Ant Colony Optimization	遺伝的アルゴリズム
分野	最適化	最適化
系統	Process / pipeline	Process / pipeline
提唱年	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1975
提唱者	—	John Henry Holland
種類	Metaheuristic — swarm intelligence	Population-based metaheuristic
原典	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
別名	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
関連	5	5
概要	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.	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.