Hybridizing Genetic Algorithms and Tabu Search for Multimodal Function Optimization

摘要

The hybridization of genetic algorithms (GAs) and Tabu Search (TS) is one of the traditional problems in function optimization in the GA literature. However, most proposed methods so far have utilized GAs to explore global candidates and TS to exploit local optima. Unlike such methods, this paper discusses new algorithms to directly store individuals into multiple tabu lists during GA-iterations. The paper describes the basic idea, algorithms, experimental results, and their practical applications for social simulation and electric equipments design.