Xuezhi Yue

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Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In(More)
In the paper, particle gradient multi-objective evolutionary algorithm (PGMOEA) on GPU is presented. PGMOEA extends the classical particle dynamic multi-objective evolutionary algorithm by incorporating the gradient information of each particle from evolutionary programming. We perform experiments to compare PGMOEA on GPU with PGMOEA on CPU and demonstrate(More)
In this paper, a new multi-objective evolutionary algorithm for solving high complex multi-objective problems is presented based on the rule of energy minimizing and the law of entropy increasing of particle systems in phase space, Through the experiments it proves that this algorithm can quickly obtains the Pareto solutions with high precision and uniform(More)
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