改进蝙蝠算法在多目标优化中的应用

The application of the improved bats algorithm in multi-objective optimization

在模拟退火的高斯扰动蝙蝠优化算法(SAGBA)的基础上,结合解决多目标优化问题的算法技术,探讨了2种改进的多目标蝙蝠算法——基于动态加权的SAGBA算法(DWASAGBA)和基于向量估计的SAGBA算法(VESAGBA),并对算法进行了仿真实验.结果表明,SAGBA算法所得到的解集分布均匀,能够得到测试函数较为准确的Pareto曲线.

Combined the SAGBA algorithm with the technology of solving the problem of multi-objective optimization, two kinds of improved multi-objective bat algorithm the SAGBA algorithm based on dynamic weighted （DWASAGBA） and the SAGBA algorithm based on vector estimation（VESAGBA） are discussed. The simulation experiment results show that the solution set distribution is uniform, and can get a relatively accurate Pareto curve,so the SAGBA algorithm is effective in solving multi-objective optimization problems.