Abstract
Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.
Highlights
Optimization problems exist in all kinds of engineering and scientific areas
We propose a new Multi-objective evolutionary algorithm (MOEA) to solve the multiobjective optimization problem (MOP) more effectively, in which an enhanced elitism makes the nondominated solutions play the better guide role and an entropy-based strategy is applied to preserve the diversity of the population
Combing the basic evolutionary algorithm and the tradition of the method producing offspring in genetic algorithm, we proposed the entropy-based multiobjective evolutionary algorithm with an enhanced elite mechanism (E-MOEA)
Summary
Optimization problems exist in all kinds of engineering and scientific areas. When there is more than one objective in an optimization problem, it is called a multiobjective optimization problem (MOP). Wang et al proposed the MOSADE algorithm [13], which combines the self-adaptive differential evolution and the crowding entropy-based diversity measure to obtain the nondominated solution set In this algorithm, every solution can calculate its crowding degree through the improved the information entropy formula according to solutions’ distribution. We propose a new MOEA to solve the MOP more effectively, in which an enhanced elitism makes the nondominated solutions play the better guide role and an entropy-based strategy is applied to preserve the diversity of the population. We call it an entropy-based multiobjective evolution algorithm with an enhanced elitism, namely, E-MOEA in brief. Experimental results on the 2-objective problems and the 3-objective problems show that the novel algorithm has better performance in both convergence and diversity, compared with NSGA-II, SPEA2 and MOSADE
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