Adoptive population differential evolution with local search for solving large scale global optimization
Sheng-Ta Hsieh, Shih-Yuan Chiu, Shi-Jim Yen · 2012
Due to real-world optimization problems become increasingly complex. Algorithms are with higher efficiency and higher solution searching ability for finding global optimal solution in reasonable computing time is always needed. Thus, in this paper, an improved DE is proposed for solving large scale global optimization. The proposed method is incorporated with the population manager to eliminate redundant particles or to hire new ones or to maintain population size according to the solution searching status to make the process more efficient. Besides, a local search strategy is also involved to enhance population's solution search ability. Experiments were conducted on ten CEC 2012 test functions to present performance of the proposed method. The proposed method exhibits better performance than other three related works in solving most test functions.