JCSE, vol. 8, no. 4, pp.199-206, December, 2014

DOI: http://dx.doi.org/10.5626/JCSE.2014.8.4.199

A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems

Wu Deng1,2,3,4, Han Chen1,2, and He Li1,4
1Software Institute, Dalian Jiaotong University, Dalian, China/ 2The Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China/ 3The Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, China/ 4Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, China

Abstract: The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

Keyword: Genetic algorithm; Ant colony optimization algorithm; Multi strategies; Hybrid evolutionary

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