Multiobjective optimization with is an element of-constrained method for solving real-parameter constrained optimization problems
Multiobjective optimization with is an element of-constrained method for solving real-parameter constrained optimization problems
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多目标优化是一种求解实参数约束优化问题的约束元方法
DOI：
10.1016/j.ins.2018.07.071
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发表时间：
2018
影响因子：
8.1
通讯作者：
Zhang Jun
中科院分区：
文献类型：
--
作者：
Ji Jing-Yu;Yu Wei-Jie;Gong Yue-Jiao;Zhang Jun
本文提出了一种新的算法来解决现实世界中的约束优化问题，它混合了多目标优化技术与一个约束优化方法。首先，手头的约束优化问题转化为一个双目标优化问题。通过这种转换，可以利用多目标优化技术在约束优化领域的优势，平衡种群多样性和收敛性。同时，采用了带约束的方法，使种群不断向约束优化问题的可行域演化。在我们提出的算法中，差分进化作为一个搜索引擎，在每一代创建后代。此外，不同的变异算子的组合已被开发，以提高搜索能力和种群收敛在不同的阶段。我们的方法的性能进行评估的64个基准测试功能，从三个流行的测试套装。实验结果表明，我们所提出的方法是能够获得高质量的解决方案的大多数基准测试功能，与其他一些国家的最先进的约束优化算法相比。
This paper develops a novel algorithm to solve real-world constrained optimization problems, which hybridizes multiobjective optimization techniques with an ϵ-constrained method. First, a constrained optimization problem at hand is transformed into a bi-objective optimization problem. By the transformation, the advantage of multiobjective optimization techniques can be utilized in the constrained optimization area to balance population diversity and convergence. Meanwhile, the ϵ-constrained method is applied, which keeps the population evolving toward feasible region of the constrained optimization problem. In our proposed algorithm, the differential evolution is employed as a search engine to create offspring at each generation. Further, different combinations of mutation operators have been developed to improve the search ability and the population convergence at different stages. The performance of our approach is evaluated on 64 benchmark test functions from three popular test suits. Experimental results demonstrate that our proposed approach is capable of obtaining high-quality solutions on the majority of benchmark test functions, when compared with some other state-of-the-art constrained optimization algorithms.