Group Search Optimization to solve Traveling Salesman Problem

被引：0

作者：

Akhand, M. A. H. ^{[1
]}

Junaed, A. B. M. ^{[1
]}

Hossain, Md. Forhad ^{[1
]}

Murase, K. ^{[2
]}

机构：

[1] Khulna Univ Engn & Technol, Dept Comp Sci & Engn, Khulna, Bangladesh

[2] Univ Fukui, Dept Human & Artificial Intelligent Syst, Fukui, Japan

来源：

2012 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (ICCIT) | 2012年

关键词：

Traveling Salesman Problem; Group Search Optimizer; Swap Sequence and Swap Operator;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The goal of Traveling Salesman Problem (TSP) is to find the shortest circular tour visiting every city exactly once. TSP has many real world applications and a number of methods have been investigated to solve TSP. Recently, nature inspired algorithms are also attracted to solve it. Here we studied Group Search Optimizer(GSO), the recently proposed nature inspired algorithm, to solve TSP. GSO is a population based optimization technique on the metaphor of producer-scrounger based social behavior of animals where producer searches for finding foods and scrounger searches for joining opportunities. GSO has found as an efficient method for solving function optimization problems for which it modeled. In this study we employ the concept of Swap Operator (SO) and Swap Sequence (SS) to modify GSO for TSP. The modified GSO (mGSO) was tested on a number of benchmark TSPs and results compared with some existing approaches. mGSO has shown best results (best tour cost) for some problems and competitive performance in other cases.

引用

页码：72 / 77

页数：6

共 14 条

[1]

[Anonymous], 2002, Mathware and Soft Computing

[2]

[Anonymous], 1999, Swarm Intelligence

[3] PRODUCERS AND SCROUNGERS - A GENERAL-MODEL AND ITS APPLICATION TO CAPTIVE FLOCKS OF HOUSE SPARROWS [J].

BARNARD, CJ ;

SIBLY, RM .

ANIMAL BEHAVIOUR, 1981, 29 (MAY) :543-550

[4] FORAGING AND FLOCKING STRATEGIES - INFORMATION IN AN UNCERTAIN ENVIRONMENT [J].

CLARK, CW ;

MANGEL, M .

AMERICAN NATURALIST, 1984, 123 (05) :626-641

[5] Food exploitation: searching for the optimal joining policy [J].

Giraldeau, LA ;

Beauchamp, G .

TRENDS IN ECOLOGY & EVOLUTION, 1999, 14 (03) :102-106

[6]

Goldberg D. E., 1998, GENETIC ALGORITHMS

[7] Fractal characteristic of silica xerogels with different additives [J].

He, Fei ;

He, Xiaodong ;

Li, Yao .

2006 1ST IEEE INTERNATIONAL CONFERENCE ON NANO/MICRO ENGINEERED AND MOLECULAR SYSTEMS, VOLS 1-3, 2006, :1272-+

[8] Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior [J].

He, S. ;

Wu, Q. H. ;

Saunders, J. R. .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (05) :973-990

[9]

Kennedy J., 1995, PROC 6 INT S MICROMA, P39, DOI DOI 10.1109/MHS.1995.494215

[10]

Krause J., 2002, OXFORD SERIES ECOLOG

← 1 2 →