计算机科学与探索2024，Vol.18Issue(6)：1513-1525,13.DOI:10.3778/j.issn.1673-9418.2304026
极限学习决策网络指导的多目标粒子群算法
Multi-objective Particle Swarm Optimization Algorithm Guided by Extreme Learning Decision Network
摘要
Abstract
When solving multi-objective optimization problems,particle swarm optimization algorithms usually em-ploy preset example selection methods and search strategies,which cannot be adjusted according to specific optimi-zation states.In the face of different optimization problems,inappropriate search strategies cannot effectively guide the population,resulting in low search performance of the population.To solve the above problems,a multi-objective particle swarm optimization algorithm guided by extreme learning decision network(ELDN-PSO)is proposed.First of all,the multi-objective optimization problem is decomposed into several scalar subproblems,and an extreme learning decision network is constructed.The network takes the particle position as input,and selects appropriate search actions for each particle according to the optimization state.The fitness change of a particle on the subprob-lem is obtained as the training sample for the reinforcement learning,and the training speed is improved by extreme learning machine.In the process of optimization,the network is automatically adjusted according to the optimiza-tion states,and it selects the appropriate search strategy for the particles at different search stages.Secondly,the non-dominated solutions in the multi-objective optimization problem are difficult to compare.Thus,the leadership of each solution is quantified into a comparable value,so that the examples are more clearly selected for the particles.In addition,an external archive is used to store better particles to maintain the quality of the solutions and guide the population.Comparative experiments are carried out on the ZDT and DTLZ test functions.The results show that ELDN-PSO can effectively cope with different Pareto front shapes,improving the optimization speed as well as the conver-gence and diversity of the solutions.关键词
粒子群优化/极限学习机/多目标优化/目标分解/加速系数Key words
particle swarm optimization/extreme learning machine/multi-objective optimization/objective decom-position/acceleration coefficient分类
信息技术与安全科学引用本文复制引用
张一帆,宋威..极限学习决策网络指导的多目标粒子群算法[J].计算机科学与探索,2024,18(6):1513-1525,13.基金项目
国家自然科学基金(62076110) （62076110）
江苏省自然科学基金(BK20181341) （BK20181341）
中央高校基本科研业务费专项资金(JUSRP221027).This work was supported by the National Natural Science Foundation of China(62076110),the Natural Science Foundation of Jiangsu Province(BK20181341),and the Fundamental Research Funds for the Central Universities of China(JUSRP221027). （JUSRP221027）
