Title: A new wolf colony search algorithm based on search strategy for solving travelling salesman problem

Authors: Yang Sun; Lin Teng; Shoulin Yin; Hang Li

Addresses: Software College, Shenyang Normal University, No. 253, HuangHe Bei Street, HuangGu District, P.C. 110034, Shenyang, China ' Software College, Shenyang Normal University, No. 253, HuangHe Bei Street, HuangGu District, P.C. 110034, Shenyang, China ' Software College, Shenyang Normal University, No. 253, HuangHe Bei Street, HuangGu District, P.C. 110034, Shenyang, China ' Software College, Shenyang Normal University, No. 253, HuangHe Bei Street, HuangGu District, P.C. 110034, Shenyang, China

Abstract: Though many intelligence algorithms are used for travelling salesman problem (TSP), the main objective of this paper is to execute new approach to obtain significant improvements. This paper proposes an improved wolf colony search algorithm based on search strategy. First, we introduce interaction strategy into travel behaviour and calling behaviour to promote the communication between artificial wolves, which can improve the information acquirement for wolves and enhance the exploring ability of wolves. Second, we present adaptive siege strategy for siege behaviour, which guarantees that the new algorithm can obtain better collaborative search feature. Therefore, the range of wolf siege constantly decreases and the mining ability of wolf algorithm increases with the new strategy. Finally, experiments are carried out to verify the effectiveness of new method compared with other algorithms for TSP problems. The results show that the improved wolf colony search algorithm has higher solving accuracy, faster convergence speed.

Keywords: wolf colony search algorithm; WA; search strategy; interaction strategy; adaptive siege strategy; siege behaviour; travelling salesman problem; TSP.

DOI: 10.1504/IJCSE.2019.10017816

International Journal of Computational Science and Engineering, 2019 Vol.18 No.1, pp.1 - 11

Received: 17 Mar 2017
Accepted: 03 May 2017

Published online: 14 Dec 2018 *

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