Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems
Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems
复制标题
连续域蚁群算法解决函数优化问题的实验评估
DOI：
10.1007/978-3-030-00533-7_30
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Toshihide Ibaraki
中科院分区：
文献类型：
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作者：
Ryouei Takahashi;Yukihiro Nakamura;Toshihide Ibaraki
利用Michaelwicz函数等10个标准多峰测试函数对一种新的蚁群算法求解函数优化问题（FOP）进行了实验评价。在中，蚂蚁在二分搜索空间中搜索解，并通过逐步定位搜索空间来提高解的精度。实验结果表明，该算法能在搜索精度和效率之间取得平衡，并能有效地减少种群规模，是一种基于真实的搜索空间的蚁群算法。协方差矩阵自适应进化策略（CMA-ES）在计算时间上具有上级优势，但解的精度较低;遗传算法（GA）在最优解的获得率上具有上级优势，但性能较弱。
A new Ant Colony Optimizationto solve function optimization problems (FOP) is evaluated experimentally by using ten standard multimodal test functions such as Michaelwicz’s function. In, ants search for solutions in binary search space and can improve the accuracy of solutions by the stepwise localization of search space. Experiments show thatcan keep the balance between accuracy and efficiency to search for optimum solutions, and that it can reduce the population size of, which is a preceding ACO based on real search space. It is also shown that Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) is superior in computational time but lacks the accuracy of solutions, and that Genetic Algorithm (GA) is superior in the ratio of getting the optimum solutions but weak in the performance.