Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

Yanyan Tan, Xue Lu, Yan Liu, Qiang Wang and Huaxiang Zhang

Mathematical Problems in Engineering, 2019, vol. 2019, 1-18

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6943921

DOI: 10.1155/2019/6943921

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