Journal
INFORMATION SCIENCES
Volume 485, Issue -, Pages 200-218Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.01.066
Keywords
Dynamic multiobjective optimization; Evolutionary algorithms; Similar environment; Memory; Prediction
Categories
Funding
- National Natural Science Foundation of China [61871272, 61471246, 61673331]
- Project of Department of Education of Guangdong Province [2016KTSCX121]
- Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions [Yq2013141]
- Guangdong Special Support Program of Top-Notch Young Professionals [2014TQ01X273]
- Shenzhen Scientific Research and Development Funding Program [JCYJ20170302154227954, JCGG20170414111229388, ZYC201105170243A]
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Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs. (C) 2019 Elsevier Inc. All rights reserved.
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