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Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems
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DOI：10.1007/s10489-014-0619-9.png)
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Abstract
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In this paper, we propose a modified differential evolution (DE) based algorithm for solving multi-objective optimization problems (MOPs). The proposed algorithm, called multi-objective DE with dynamic selection mechanism (DSM), i.e., MODE-DSM, modifies the general DE mutation operation to produce a population at each generation. To determine and evaluate a better spread of the non-dominated solution, a DSM with a new cluster degree measure is developed. The DSM is also used to select diverse non-dominated solutions. The performance of the proposed algorithm is evaluated against seventeen bi-objective and two tri-objective benchmark test problems. The experimental results show that the proposed algorithm achieves better convergence to the Pareto-optimal front as well as better diversity on the final non-dominated solutions than the other five multi-objective evolutionary algorithms (MOEAs). It suggests that the proposed algorithm is promising in dealing with MOPs. The ability of MODE-DSM with small population and the sensitivity of MODE-DSM have also been experimentally investigated in this paper.
Keywords:
Differential evolution
Multi-objective optimization problems
Non-dominated
Pareto-optimal front
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