Particle swarm optimization algorithm with multi methods argument
Kai Zhang, Song Jinchun, Ren Guang-an, Jia Shi · AI Communications · 2016
Particle swarm optimization (PSO) is well known for dealing with complex nonlinear problems. In recent years, many researchers developed improved PSO algorithms to enhance the search and convergence ability. However, when dealing with the engineering control problems, the goal function is usually u nknown and discrete. Thus, an algorithm with good search ability and fast convergence speed is required. This paper presents a new development algorithm called multi methods argument particle swarm optimization (MMAPSO). This algorithm uses an argument strategy to draw the merits of some search methods, such as chaotic search, cloud search and gradient descent search. This strategy debates the search method according to the best position of particle and average convergence speed. The experiments are conducted on uni-modal functions, multi-modal functions and noisy functions. The results demonstrate the superiority of MMAPSO algorithm on twelve functions when compared with other six algorithms.