基于修正萤火虫算法的多模函数优化

Multimodal function optimization based on modified glowworm swarm optimization

萤火虫优化(glowworm swarm optimization,GSO)算法是一种计算多模函数优化问题的新型算法,该算法和蚁群优化、粒子群优化一样,都是一种群智能算法。针对GSO算法在优化多模函数时收敛速度慢、求解精度不高和发现峰值率低的缺点,首先在算法中采用变步长的运动策略,使得步长随着迭代时间自适应地逐渐减小;其次采用较小的初始决策范围值;最后添加了萤火虫的自探索机制。改进后的学习行为更符合自然界生物的学习规律,更有利于萤火虫发现问题的所有局部最优解。利用标准测试函数对修正后的萤火虫算法进行测试,仿真结果表明,修正的萤火虫算法具有良好的收敛性和计算精度,在寻找多模函数的峰值个数时显示出很大的优势。

Glowworm swarm optimization (GSO) is a novel algorithm for the simultaneous computation of multiple optima of mul timodal functions, which is a swarm intelligence based optimization algorithm, such as ant colony optimization (ACO) and parti- cle swarm optimization (PSO). A modified glowworm swarm optimization algorithm is proposed to solve the problems of GSO in slow convergence speed, low computational accuracy and low peaks discovery rate. Varlahle step-size movement strategy, the smaller initial value of decision range and the self-exploration behavior of glowworms are introduced. In this way, the behavior of glowworms accorded with the biological natural law evens more, and easily found multiple optima of a given multimodal function. Simulation results show that this modified optimization strategy has nice convergence ability and high precision, and in capturing multiple optima of multimodal functions, modified GSO performs very well in terms of the number of peaks captured.