多元优化算法及其收敛性分析
李宝磊;施心陵;苟常兴;吕丹桔;安镇宙;张榆锋
【期刊名称】《自动化学报》 【年(卷),期】2015(000)005
【摘要】In this paper, we present a swarm intelligent algorithm and study its convergence property. We name it as multivariant optimization algorithm (MOA) because its multiple searchers (atoms) are variant in responsibilities. The solution space is searched through global-local search iterations which are based on the collaboration and coordination between the local and global search groups in the MOA. In each iteration, the global atoms explore the whole solution space to locate potential areas and then multiple local groups with different numbers of local search atoms are allotted to these potential areas for different levels of refinements. The historical better solutions are recorded in a structure made up of a queue and some stacks, according to a certain rule. The candidate solutions in the structure are improved iteratively and will converge to the optimal solutions. A theoretical convergence analysis based on Markov process shows that MOA converges to more than one global optimal solutions with probability 1. To evaluate its convergence property, MOA and other five frequently compared algorithms are employed to locate the optimal solutions of thirteen two-dimensional and ten-dimensional benchmark functions. The results
多元优化算法及其收敛性分析
