The National Natural Science Foundation of China, No. 11372061, 2014-2016
Though multi-objective evolutionary algorithms (MOEAs) are capable of satisfying the demands arising from the new advancements in structural topology optimization on global optimization, black-box function optimization, combinatorial optimization and multi-objective optimization, the necessity of applying them to this field still depends on their convergence and computational efficiency. In the purpose of revealing competent algorithms on these two aspects, the Pareto optimum solutions for widely collected and modified multi-objective topology optimization (MOTO) benchmarks are rigorously derived and the correspondence between problem characteristics and solving hardness are established. Then we seek for a general performance assessment methodology tailor-made for examining the convergence and efficiency of MOEAs on MOTO. In the subsequent comparative study, the state of the art of MOEAs’ performance on MOTO is revealed by finding out promising real/binary metaheuristics, population updating mechanism and constraint handling technique. All these techniques are tested on large scale problems in conjunction with various co-evolution mechanisms. Finally, a new multi-objective co-evolution algorithm would be proposed. The significance of this study lies in the simultaneous examination of MOEAs’ convergence and efficiency on MOTO. Hence, it does not only contribute to the theoretical foundation of solving topology optimization problems using MOEAs, but also provide the possibility of high efficiently solving large scale and complex practical topology optimization problems.