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Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge
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DOI：10.1109/TEVC.2022.3140265.png)
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The decomposition-based evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto-optimal front (POF) of many multiobjective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point-oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a 3-D plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: 1) the central region; 2) the middle region; and 3) the edge region. Second, a dynamic transfer criterion of the reference point is developed based on the population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on 16 benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms.
Keyword:
Statistics
Sociology
Optimization
Convergence
Pareto optimization
Optical fibers
Shape
Decomposition
dynamic transfer reference point
local objective space
multiobjective optimization
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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