Grey Wolf Optimizer

摘要: This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of in nature. Four types such as alpha, beta, delta, omega are employed for simulating hierarchy. In addition, three main steps hunting, searching prey, encircling attacking implemented. is then benchmarked on 29 well-known test functions, results verified comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), Strategy (ES). show that able to provide very competitive compared these meta-heuristics. paper also considers solving classical engineering design problems (tension/compression spring, welded beam, pressure vessel designs) presents real application proposed method field optical engineering. prove applicable challenging unknown search spaces.