In the real-world engineering optimization,there are a number of constrained problems,and some of them have time-consuming objective and constrained functions,resulting the evolutionary optimization algorithms are not able to be applied into this kind of problems.Thus,in order to achieve a feasible solution with better objective value in a limited computational budget,the radial basis function surrogate models are trained for objective and constrain-ed functions,respectively.The propagation strategy of each solution will be adaptively determined according to its approximated values on objective and constrained functions,which is expected to improve a good feasible solution.The experimental results on seven benchmark problems and three engineering test problems show that compared to some state-of-the-art algorithms,the proposed method does not need to ensure that there is at least one feasible so-lution in the initial population,and can find better solutions in a limited computational cost.
关键词
约束优化/进化算法/径向基函数/昂贵单目标
Key words
constrained optimization/evolutionary algorithms/radial basis function/expensive single target
维普
万方数据