Tri-Goal Evolution Framework for Constrained Many-Objective Optimization

doi:10.1109/TSMC.2018.2858843

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Tri-Goal Evolution Framework for Constrained Many-Objective Optimization

2019-01-01

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DOI：10.1109/TSMC.2018.2858843

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Abstract

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It is generally accepted that the essential goal of many-objective optimization is the balance between convergence and diversity. For constrained many-objective optimization problems (CMaOPs), the feasibility of solutions should be considered as well. Then the real challenge of constrained many-objective optimization can be generalized to the balance among convergence, diversity, and feasibility. In this paper, a tri-goal evolution framework is proposed for CMaOPs. The proposed framework carefully designs two indicators for convergence and diversity, respectively, and converts the constraints into the third indicator for feasibility. Since the essential goal of constrained many-objective optimization is to balance convergence, diversity, and feasibility, the philosophy of the proposed framework matches the essential goal of constrained many-objective optimization well. Thus, it is natural to use the proposed framework to deal with CMaOPs. Further, the proposed framework is conceptually simple and easy to instantiate for constrained many-objective optimization. A variety of balance schemes and ranking methods can be used to achieve the balance among convergence, diversity and feasibility. Three typical instantiations of the proposed framework are then designed. Experimental results on a constrained many-objective optimization test suite show that the proposed framework is highly competitive with existing state-of-the-art constrained many-objective evolutionary algorithms for CMaOPs.

Keywords:

Convergence

Linear programming

Pareto optimization

Stochastic processes

Evolutionary computation

Computer science

Constrained many-objective optimization

constraint handling

convergence

diversity

feasibility

tri-goal evolution (TiGE)

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