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A modified dual-population approach for solving multi-objective problems

Vu, VT and Bui, TL and Nguyen, TT A modified dual-population approach for solving multi-objective problems. In: 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2017) . (21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), 15th - 17th November 2017, Hanoi, Vietnam). (Accepted)

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Abstract

Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2). In DPP2, while populations are trying to achieve both criteria, the priority being set for these criteria will be different. One population focuses on achieving better convergence (by using pareto-based ranking scheme), while the other is for ensuring the population diversity (by using the decomposition-based method). After that, we use a cooperation mechanism to integrate the two populations and create a new combined population with hopes of having both characteristics (i.e. converged and diverse). Performance of DPP2 is examined on the well-known benchmarks of multiobjective optimization problems (MOPs) using the hypervolume (HV), the generational distance (GD), the inverted generational distance (IGD) metrics. In comparison with the original version DPP algorithm, experimental results indicated that DPP2 can significantly outperform DPP on the benchmark problems with stable results.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics
T Technology > TJ Mechanical engineering and machinery
Divisions: Maritime and Mechanical Engineering
Publisher: IEEE
Date Deposited: 05 Oct 2017 10:13
Last Modified: 05 Oct 2017 10:13
URI: http://researchonline.ljmu.ac.uk/id/eprint/7286

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