A Puzzle-Based Genetic Algorithm with Block Mining and Recombination Heuristic for the Traveling Salesman Problem

In this research, we introduce a new heuristic approach using the concept of ant colony optimization （ACO） to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 27; no. 5; pp. 937 - 949
Main Author 张百栈 黄伟修 张真真
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.09.2012
Springer Nature B.V
Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan, China%Department of Computer Science, Xiamen University, Xiamen 361005, China
Subjects
Algorithms
Analysis
Ant colony optimization
Archives & records
Artificial chromosomes
Artificial Intelligence
Blocking
Chromosomes
Cities
Computer Science
Convergence
Data Structures and Information Theory
Genetic algorithms
Heuristic
Information Systems Applications (incl.Internet)
Optimization
Optimization techniques
Population
Regular Paper
Software Engineering
Studies
Theory of Computation
Traveling salesman problem
人工染色体
启发式遗传算法
开采
技术组成
旅行商问题
荒料
过早收敛
重组
traveling salesman problem
block recombination
blocks mining
artificial chromosome
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-012-1275-3

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Summary:In this research, we introduce a new heuristic approach using the concept of ant colony optimization （ACO） to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities （nodes） to be visited. The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome. The generated artificial chromosomes （ACs） will then be injected into a standard genetic algorithm （SGA） to speed up the convergence. The proposed method is called ＂Puzzle-Based Genetic Algorithm＂ or ＂p-ACGA＂. We demonstrate that p-ACGA performs very well on all TSPLIB problems, which have been solved to optimality by other researchers. The proposed approach can prevent the early convergence of the genetic algorithm （GA） and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.
Bibliography:11-2296/TP
artificial chromosome, blocks mining, block recombination, traveling salesman problem
In this research, we introduce a new heuristic approach using the concept of ant colony optimization （ACO） to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities （nodes） to be visited. The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome. The generated artificial chromosomes （ACs） will then be injected into a standard genetic algorithm （SGA） to speed up the convergence. The proposed method is called ＂Puzzle-Based Genetic Algorithm＂ or ＂p-ACGA＂. We demonstrate that p-ACGA performs very well on all TSPLIB problems, which have been solved to optimality by other researchers. The proposed approach can prevent the early convergence of the genetic algorithm （GA） and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1275-3