Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies. (6th August 2019)

• Record Type: Journal Article
• Authors: Tan, Yanyan; Lu, Xue; Liu, Yan; Wang, Qiang; Zhang, Huaxiang
• Other Names: Jędrzejowicz Piotr Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-08-06
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/6943921
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.