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Hybrid selection based multi/many-objective evolutionary algorithm
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DOI：10.1038/s41598-022-10997-0.png)
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摘要
En 中文
In the last decade, numerous multi/many-objective evolutionary algorithms (MOEAs) have been proposed to handle multi/many-objective problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art MOEAs is demonstrated through experimental simulations on DTLZ and WFG test suites with up to 10 objectives.
Keyword:
NONDOMINATED SORTING APPROACH
OPTIMIZATION
PERFORMANCE
MOEA/D
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期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
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An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints使用基于参考点的非支配排序方法的进化多目标优化算法，第一部分: 解决框约束问题
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