An Improved Hybrid Genetic Algorithm with a New Local Search Procedure

作者：Wan Wen^*; Birch Jeffrey B

来源：Journal of Applied Mathematics, 2013, 103591.

DOI：10.1155/2013/103591

One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the tradeoff between global and local searching (LS) as it is the case that the cost of an LS can be rather high. This paper proposes a novel, simplified, and efficient HGA with a new individual learning procedure that performs a LS only when the best offspring (solution) in the offspring population is also the best in the current parent population. Additionally, a newLSmethod is developed based on a three-directional search (TD), which is derivative-free and self-adaptive. The new HGA with two different LS methods (the TD and Neld-Mead simplex) is compared with a traditional HGA. Four benchmark functions are employed to illustrate the improvement of the proposed method with the new learning procedure. The results show that the new HGA greatly reduces the number of function evaluations and convergesmuch faster to the global optimum than a traditionalHGA. The TD local searchmethod is a good choice in helping to locate a global %26quot;mountain%26quot; (or %26quot;valley%26quot;) but may not performthe Nelder-Mead method in the final fine tuning toward the optimal solution.

• 出版日期2013