摘要
To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.
出处
《国际计算机前沿大会会议论文集》
2021年第1期283-299,共17页
International Conference of Pioneering Computer Scientists, Engineers and Educators(ICPCSEE)