A New Evolutionary Algorithm with Deleting and Jumping Strategies for Global Optimization

For global optimization problems with a large number of local optimal solutions, evolutionary algorithms are efficient parallel algorithms, but they drops into local optimum easily, therefore their efficiency and effectiveness will be much reduced. In this paper, first, a new deleting strategy is proposed that can eliminate all local optimal solutions no better than this obtained local optimal solution. Second, when algorithm drops into a local optimal solution, a new jumping strategy is proposed that can jump out of the current local optimal solution and then find a better local optimal solution. Based on the above, a new algorithm called evolutionary algorithm with deleting and jumping strategies (briefly, EADJ) is proposed, and the algorithm convergence is proved theoretically. The simulations are made on 25 standard benchmark problems, and the results indicate the proposed deleting strategy and jumping strategy are effective; further, the proposed algorithm is compared with some well performed existing algorithms, and the results indicate the proposed algorithm EADJ is more effective and efficient.