A Novel Hybrid Optimization Algorithm Based on Genetic Algorithm and Tabu Search

Abstract

Genetic Algorithm (GA) and Tabu Search (TS) are two well-known optimization algorithms in heuristic learning. Each has its merits, pitfalls, and application domains. Many studies were in an attempt to combine them in order to enhance the performance. A common approach was to perform these two algorithms by turns without modifying their original structures. In this paper, we propose a novel hybrid algorithm, called TGA, which incorporates the operators of GA with memory structure and search strategy of TS. The traveling salesman problem (TSP) is used as a benchmark to compare the performance of TGA, GA, and TS. Experimental results demonstrate that TGA outperforms GA and TS in terms of convergence speed and solution quality