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A decomposition-based multi-objective evolutionary algorithm with quality indicator
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DOI：10.1016/j.swevo.2017.11.004.png)
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The issue of integrating preference information into multi-objective optimization is considered, and a multi objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.
Keyword:
Evolutionary computations
Multi-objective optimization
Algorithm diversity
Decomposition
Indicator-based
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