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控制理论与应用 2010
Efficient evolutionary algorithm for unconstraint global optimization
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Abstract:
In solving global optimization problems, evolutionary algorithms converge slowly and tend to be trapped in local optimal solutions. A crossover operator is designed which searches the descent-directions based on the relationship between the best individual and the others in the population. Once it finds an individual better than the best one in the population, the objective function is further optimized by using the projection of the intersection of two constructed lines, so that the function can decrease faster. A method is presented to generate the initial population for the crossover operator. To improve the performance of the algorithm, a mutation operator which increases the convergence rate and avoids to be trapped in the local optima is given. Based on all these, an evolutionary algorithm for global optimization is proposed and its global convergence is proved. Numerical results show the efficiency of the proposed algorithm for all test functions.