A Novel Hybrid Optimization Algorithm Based on GA and ACO for Solving Complex Problem

Abstract

In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algorithm is used to dynamically balance the global search ability and local search ability in order to improve the convergence speed. Finally, some complex benchmark functions are selected to prove the validity of the proposed NHGACO algorithm. The experiment results show that the proposed NHGACO algorithm can obtain the global optimal solution and avoid the phenomena of the stagnation, and take on the fast convergence and the better robustness.

Web page ranking using ant colony optimisation and genetic algorithm for effective information retrieval

“…In Gao et al (2015) hybrid of GA and ACO is used for solving complex problems. GA is used to update the global optimum solution and ACO is used for balancing the global and local search ability for improving the convergence speed.…”