Browsing by Author "Yen, Gary G."

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    Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments
    (IEEE, 2023-02-02) Chen, Qingda; Ding, Jinliang; Yen, Gary G.; Yang, Shengxiang; Chai, Tianyou
    Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multi-population evolution based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.
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    Utilizing cumulative population distribution information in differential evolution
    (Elsevier, 2016-07-15) Wang, Yong; Liu, Zhizhong; Li, Jianbin; Li, Han-Xiong; Yen, Gary G.
    Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms. In general, DE does not exploit distribution information provided by the population and, as a result, its search performance is limited. In this paper, cumulative population distribution information of DE has been utilized to establish an Eigen coordinate system by making use of covariance matrix adaptation. The crossover operator of DE implemented in the Eigen coordinate system has the capability to identify the features of the fitness landscape. Furthermore, we propose a cumulative population distribution information based DE framework called CPI-DE. In CPI-DE, for each target vector, two trial vectors are generated based on both the original coordinate system and the Eigen coordinate system. Then, the target vector is compared with these two trial vectors and the best one will survive into the next generation. CPI-DE has been applied to two classic versions of DE and three state-of-the-art variants of DE for solving two sets of benchmark test functions, namely, 28 test functions with 30 and 50 dimensions at the 2013 IEEE Congress on Evolutionary Computation, and 30 test functions with 30 and 50 dimensions at the 2014 IEEE Congress on Evolutionary Computation. The experimental results suggest that CPI-DE is an effective framework to enhance the performance of DE.