Browsing by Author "Zhou, Jinlong"

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    An infeasible solutions diversity maintenance epsilon constraint handling method for evolutionary constrained multiobjective optimization
    (Springer, 2021-05-25) Zhou, Jinlong; Zou, Juan; Zheng, Jinhua; Yang, Shengxiang; Gong, Dunwei; Pei, Tingrui
    It is well known that it is very difficult to solve constrained multiobjective optimization problems. Such problems not only need to optimize the objective function but also need to consider the constraints. The epsilon constraint handling method is commonly used, which releases the degree of constraint violations by defining a gradually decayed epsilon. However, for the solutions whose overall constraint violations degree is greater than epsilon, the original epsilon constraint handling method cannot guarantee the diversity of solutions and only constraint violations are considered. To solve this issue, this paper proposed an infeasible solutions diversity maintenance strategy for solutions whose constraint violations degree is greater than epsilon. The experimental results show that our proposed algorithm is very competitive with other state-of-the-art algorithms for constrained multiobjective optimization problems.
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    Niche-based and angle-based selection strategies for many-objective evolutionary optimization
    (Elsevier, 2021-04-20) Zhou, Jinlong; Zou, Juan; Yang, Shengxiang; Zheng, Jinhua; Gong, Dunwei; Pei, Tingrui
    It is well known that balancing population diversity and convergence plays a crucial role in evolutionary many-objective optimization. However, most existing multiobjective evolutionary algorithms encounter difficulties in solving many-objective optimization problems. Thus, this paper suggests niche-based and angle-based selection strategies for many-objective evolutionary optimization. In the proposed algorithm, two strategies are included: niche-based density estimation strategy and angle-based selection strategy. Both strategies are employed in the environmental selection to eliminate the worst individual from the population in an iterative way. To be specific, the former estimates the diversity of each individual and finds the most crowded area in the population. The latter removes individuals with weak convergence in the same niche. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with six state-of-the-art many-objective algorithms. Moreover, the proposed algorithm has also been verified to be scalable to deal with constrained many-objective optimization problems.