方法对比

	蝙蝠算法	布谷鸟搜索	萤火虫算法	粒子群优化 (PSO)
领域	优化	优化	优化	优化
方法族	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2010	2009	2008	1995
提出者	Xin-She Yang	—	Xin-She Yang	—
类型	Population-based swarm intelligence	Population-based metaheuristic / swarm intelligence	Swarm intelligence metaheuristic	Population-based metaheuristic / swarm intelligence
开创性文献	Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO), 65–74.	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
别名	BA, Bat-Inspired Algorithm, Echolocation-Based Optimization, Yarasa Algoritması	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
相关	3	6	5	6
摘要	The Bat Algorithm (BA) is a nature-inspired metaheuristic optimization method proposed by Xin-She Yang in 2010. It mimics the echolocation behavior of microbats to balance global exploration and local exploitation. Each artificial bat adjusts its position, velocity, and emission frequency, with loudness and pulse rate dynamically controlling the transition from broad search to refined local tuning. BA is suited to continuous and combinatorial optimization problems across engineering, scheduling, and machine learning domains.	Cuckoo Search (CS) is a population-based metaheuristic optimization algorithm introduced by Xin-She Yang and Suash Deb in 2009. It models the obligate brood-parasitism of cuckoo birds — which lay eggs in other birds' nests — combined with Lévy flight random walks that enable long-range exploration of the search space. The algorithm has proven effective in structural engineering design, machine learning hyperparameter tuning, and other continuous black-box optimization problems.	The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.	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.