Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems
Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems
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DOI：
10.3390/sym12050767
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发表时间：
2020-05-01
期刊：
影响因子：
2.7
通讯作者：
Nasr, Sarah
中科院分区：
文献类型：
--
作者：
Abo-Elnaga, Yousria;Nasr, Sarah
双层规划问题（BLPP）是由两个相互关联的层次优化问题组成的优化问题。解决 BLPP 是优化界面临的最困难的任务之一。本文提出了一种改进的遗传算法和混沌搜索来求解 BLPP。首先，该算法使用改进的遗传算法解决上层问题。遗传算法已采用新的选择技术进行了修改。新的选择技术可以帮助上层决策者在预期下层反应的情况下做出适当的决定。它将所提出的算法与求解较低层次问题的次数极少的特点区分开来，增强了算法的性能并快速收敛到解。其次，围绕改进的遗传算法解决方案应用了基于混沌理论的局部搜索。混沌局部搜索使算法能够摆脱局部解并提高对全局解的收敛性。该算法对四十个不同的测试问题进行了评估，以显示该算法的有效性。结果分析说明了新的选择技术效果和混沌搜索对算法性能的影响。将所提出的算法结果与其他最先进的算法结果进行比较，以显示所提出的算法的优越性。
Bi-level programming problem (BLPP) is an optimization problem consists of two interconnected hierarchical optimization problems. Solving BLPP is one of the hardest tasks facing the optimization community. This paper proposes a modified genetic algorithm and a chaotic search to solve BLPP. Firstly, the proposed algorithm solves the upper-level problem using a modified genetic algorithm. The genetic algorithm has modified with a new selection technique. The new selection technique helps the upper-level decision-maker to take an appropriate decision in anticipation of a lower level's reaction. It distinguishes the proposed algorithm with a very small number of solving the lower-level problem, enhances the algorithm performance and fasts convergence to the solution. Secondly, a local search based on chaos theory has applied around the modified genetic algorithm solution. Chaotic local search enables the algorithm to escape from local solutions and increase convergence to the global solution. The proposed algorithm has evaluated on forty different test problems to show the proposed algorithm effectiveness. The results have analyzed to illustrate the new selection technique effect and the chaotic search effect on the algorithm performance. A comparison between the proposed algorithm results and other state-of-the-art algorithms results has introduced to show the proposed algorithm superiority.