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针对进化算法收敛速度缓慢、容易陷早熟的问题,提出了约束多目标优化问题的一种新的快速进化算法.设计了能够从可行解空间和不可行解空间同时搜索的交叉算子,将约束条件和目标结合在一起,引入一种新的偏序关系用于比较个体之间的优劣,提出一种新的Niche值计算方法作为维持种群均匀性的主要动力,并采用已搜索解集避免了算法的重复搜索.在此基础上,设计了具有全局搜索能力的进化算法,并证明了算法的收敛性.仿真结果表明,与同类进化算法相比,该算法能够快速收敛到Pareto前沿,并能很好地维持种群的多样性.
Aiming at the problem that evolutionary algorithm converges slowly and prematurely, a new fast evolutionary algorithm for constrained multi-objective optimization problem is proposed. A crossover operator that can search simultaneously from feasible solution space and infeasible solution space is designed. Conditions and goals are combined to introduce a new partial order relationship to compare the advantages and disadvantages of individuals. A new method of calculating Niche value is proposed as the main driving force to maintain the uniformity of population. The iterative search of the algorithm is carried out.On the base of this, an evolutionary algorithm with global search capability is designed and the convergence of the algorithm is proved.The simulation results show that the proposed algorithm can converge quickly to the Pareto frontier with the same kind of evolutionary algorithms Can well maintain the diversity of the population.