An efficient hybrid technique for numerical optimization and applications
An efficient hybrid technique for numerical optimization and applications
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DOI：
10.1016/j.cie.2015.02.010
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发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Raghav Prasad Parouha;K. Das
中科院分区：
文献类型：
--
作者：
Raghav Prasad Parouha;K. Das
真实的世界优化问题是展示优化算法的潜力和有效性的主要关注点之一。在这种情况下，混合算法的两个流行的遗传算法，即差分进化（DE）和粒子群优化（PSO）从事的“三种群”的环境。最初，整个群体（按照适应度的递增顺序）被分成三个组--劣群、中群和上级群。DE用于下组和上级组，而PSO用于中组。所提出的方法被缩写为DPD，因为它使用DE-PSO-DE的人口。在DPD循环中还采用了两种策略：精英策略（保留迄今为止获得的最佳值）和非冗余搜索（提高解的质量）。此外，DE的突变策略的鲁棒性已经得到了很好的研究和合适的突变策略，为两个DE（DPD）的研究在一组现有的8个流行的突变策略，结果64个变种的DPD。通过CEC 2006、CEC 2010和5个工程设计问题的测试功能对顶层DPD进行了进一步的测试。并将其用于求解CEC 2011真实的世界优化问题。推荐的DPD的一个优秀的效率被确认超过国家的最先进的算法。
Real World Optimization Problems is one of the major concerns to show the potential and effectiveness of an optimization algorithm. In this context, a hybrid algorithm of two popular heuristics namely Differential Evolution (DE) and Particle Swarm Optimization (PSO) engaged on a ‘tri-population’ environment. Initially, the whole population (in increasing order of fitness) is divided into three groups – Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is abbreviated as DPD as it uses DE–PSO–DE on a population. Two strategies namelyElitism(to retain the best obtained values so far) andNon-redundant search(to improve the solution quality) have been additionally employed in DPD cycle. Moreover, the robustness of the mutation strategies of DE have been well studied and suitable mutation strategies for both DEs (for DPD) are investigated over a set of existing 8 popular mutation strategies which results 64 variants of DPD. The top DPD is further tested through the test functions of CEC2006, CEC2010 and 5 Engineering Design Problems. Also it is used to solve CEC2011 Real World Optimization problems. An excellent efficiency of the recommended DPD is confirmed over the state-of-the-art algorithms.