A modified glowworm swarm optimization for multimodal functions
Yuli Zhang, Xiaoping Ma, Ying Gu, Yanzi Miao · 2011
Glowworm swarm optimization (GSO) is a novel algorithm for the simultaneous computation of multiple optima of multimodal functions, which is a swarm intelligence based optimization algorithm, such as ant colony optimization (ACO) and particle swarm optimization (PSO). In the optimization of multimode functions, GSO performs very well in terms of the number of peaks captured. In this paper, we propose a modified glowworm swarm optimization algorithm. Variable step-size movement strategy and the self-exploration behavior of glowworms have been studied according to the phenomena of nature. In this way, the behavior of glowworms accords with the biological natural law even more, and easily find multiple optima of a given multimodal function. Simulation experiments on three standard multimodal functions are carried out, and the results show that this modified optimization strategy has nice convergence ability and precision. And the convergence speed of the algorithm is greatly improved.