Title :
A Hybrid Algorithm of Converse Ant Colony Optimization for Solving JSP
Author :
Song, Xiaoyu ; Cao, Yang
Author_Institution :
Sch. of Inf. & Control Eng., Shenyang Jianzhu Univ., Shenyang, China
Abstract :
A hybrid algorithm of converse ant colony optimization (HCACO) is proposed, which is used to overcome the disadvantages of the slow convergence speed and stagnation behavior when solving job shop problem (JSP). In order to improve the probability of escaping from the local optimization, we induct converse ants into the ant colony. At the same time, each solution of ACO with certain probability pursues the process of parallel enhanced SA algorithm to accelerate the coverage speed. Compared with PGA and ACO, HCACO algorithm is simulated for benchmark instances and it illustrates that the hybrid algorithm shows more better and efficient results.
Keywords :
job shop scheduling; optimisation; probability; converse ant colony optimization; hybrid algorithm; job shop problem; probability; Acceleration; Ant colony optimization; Benchmark testing; Computational modeling; Concurrent computing; Control engineering; Electronics packaging; Evolutionary computation; Job shop scheduling; Traveling salesman problems;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5366443
