The Hybrid Framework for Multi-objective Evolutionary Optimization Based on Harmony Search Algorithm
The Hybrid Framework for Multi-objective Evolutionary Optimization Based on Harmony Search Algorithm
复制标题
DOI：
10.1007/978-3-319-91337-7_13
复制
发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
Iyad Abu Doush;Mohammad Qasem Bataineh;Mohammed El-Abd
中科院分区：
文献类型：
--
作者：
Iyad Abu Doush;Mohammad Qasem Bataineh;Mohammed El-Abd
在进化多目标优化中，采用进化算法求解多个目标函数并行优化的优化问题。文献中提出了许多解决多目标优化问题的技术，包括NSGA-II、MOEA/D和MOPSO算法。和谐搜索（HS）是一种较新的启发式算法，它与非支配排序（NSHS）或多目标分解为标量子问题（MOHS/D）相结合，成功地解决了多目标问题。在本文中，使用先前提出的混合框架增强了NSHS和MOHS/D的性能。在这个框架中，每预先确定的迭代次数就测量种群的多样性。基于测量的多样性，调用局部搜索或多样性增强机制。采用HS时混合框架的效率使用ZDT， DTLZ和CEC2009基准进行了调查。实验结果证实了以HS为主要算法的混合框架的性能有所提高。
In evolutionary multi-objective optimization, an evolutionary algorithm is invoked to solve an optimization problem involving concurrent optimization of multiple objective functions. Many techniques have been proposed in the literature to solve multi-objective optimization problems including NSGA-II, MOEA/D and MOPSO algorithms. Harmony Search (HS), which is a relatively new heuristic algorithm, has been successfully used in solving multi-objective problems when combined with non-dominated sorting (NSHS) or the breakdown of the multi-objectives into scalar sub-problems (MOHS/D). In this paper, the performance of NSHS and MOHS/D is enhanced by using a previously proposed hybrid framework. In this framework, the diversity of the population is measured every a predetermined number of iterations. Based on the measured diversity, either local search or a diversity enhancement mechanism is invoked. The efficiency of the hybrid framework when adopting HS is investigated using the ZDT, DTLZ and CEC2009 benchmarks. Experimental results confirm the improved performance of the hybrid framework when incorporating HS as the main algorithm.