A multiple level competitive swarm optimizer based on dual evaluation criteria and global optimization for large-scale optimization problem

Large-scale optimization problems (LSOPs) in science and technology bring great challenges to the performance of algorithms. Although Competitive swarm optimizer (CSO) is an effective method, some shortcomings still exist when handling LSOPs, such as premature convergence. Therefore, a novel multiple level CSO with dual evaluation criteria and global optimization (DEGMCSO) is proposed to seek optimal solutions of LSOPs. In this paper, dual evaluation criteria are inserted into the multiple comparison process of the losers and winners to assist the algorithm retain more high quality particles with the potential. In addition to fitness values, adaptive selection weight fitness-distance is designed as the other criterion for selecting winners and losers according to different optimization problems. Meanwhile, a simple global optimal modification strategy is employed to get high quality global best solution. By CEC2010 and CEC2013 function suits, the results indicate DEGMCSO outperforms some popular algorithms. Finally, DEGMCSO is applied to feather selection problems of high dimension classification in the real world. The simulation results show that compared with the original CSO algorithm, DEGMCSO can find the solution of 16 functions on CEC2010 test function set which is obviously better than the CSO algorithm.