A Hybrid Algorithm of Immune Algorithm and Gradient Search for Multiple Solution Search

In recent years, many evolutionary computation methods have been proposed and applied to real-world problems. However gradient methods are still promising in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also several quasi-optimal solutions in most real-world problems. Although some methods aiming at searching for multiple solutions like genetic algorithm with sharing (GAS) and immune algorithm (IA) have been proposed, they could not find highly qualified solution in real-coded problems. This paper proposes a hybrid algorithm of real-coded IA and quasi-Newton method for multiple solution search in multimodal optimization problems. Experimental results have shown that the proposed algorithm can find optimal and quasi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal benchmark functions.