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

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CISE.2009.5366443

Filename

5366443