方法对比

	人工蜂群（ABC）优化	蚁群优化	粒子群优化 (PSO)
领域	优化	优化	优化
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2007	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1995
提出者	Dervis Karaboga & Bahriye Basturk	—	—
类型	Swarm Intelligence Metaheuristic	Metaheuristic — swarm intelligence	Population-based metaheuristic / swarm intelligence
开创性文献	Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
别名	ABC Algorithm, Bee Colony Optimization, Swarm-Based Bee Search, Yapay Arı Kolonisi	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
相关	3	5	6
摘要	Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.	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.	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.