An efficient genetic algorithm for the traveling salesman problem with precedence constraints

Moon, Chiung; Kim, Jong-Soo; Choi, Gyunghyun; Seo, Yoonho

doi:10.1016/s0377-2217(01)00227-2

Cited by 208 publications

(122 citation statements)

References 16 publications

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“…Poona and Carter [11] developed a tie break crossover (TBX), which was then modified by Choi et al [12] by combining PMX and TBX operators. Moon et al [13] proposed a new crossover operator named Moon Crossover (MX), which mimics the changes of the moon such as waxing moon → half moon → gibbous → full moon. As reported, performance of MX operator and OX operator is almost same, but OX never reached an optimal solution for all trials.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Order Crossover for Permutation Problems

Gopal¹

2015

IJIRSET

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This paper proposes a modified Order crossover operator for genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP) effectively. As the main time consuming process of crossover operator and schemata preserving process in crossover is the hole filling done in Order Crossover operator, so if the swath size is not to be too long and also depending on the problem size then it might be proven to find near optimum solutions in effective and efficient manner. The Modified Order Crossover operator constructs an offspring from a pair of parents using the existing Order Crossover operator with the enhancement on swath length (the size of a chromosome which is between two crossover sites). The efficiency of the Modified Order Crossover is compared as against some of the existing crossover operators; namely, Partially Mapped Crossover (PMX), Order Crossover (OX) & Cyclic Crossover (CX) for some benchmark TSPLIB instances. Experimental results suggest that the new crossover operator enabled improved results compared to the PMX, OX and CX for the five Travelling salesman problems tested.

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Section: Related Workmentioning

confidence: 99%

Enhanced Order Crossover for Permutation Problems

Gopal¹

2015

IJIRSET

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“…In this paper, the key concept is the topological sort. Also a new crossover operation is introduced for the proposed genetic algorithm .Experiment shows that the proposed genetic algorithm produces an optimal solution and shows better performance compared to the traditional algorithm [27]. Omar et al (2009) proposed an improved genetic algorithm to solve traveling salesman problem.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

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This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.

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“…For PCSP in supply chain, Moon et al [7] proposed GA with a priority-based encoding method to solve the scheduling problem. For problems with sequence-dependent changeover cost and precedence constraints, He & Kusiak [8] developed a heuristic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Optimisation of Precedence-Constrained Production Sequencing and Scheduling for Multiple Production Lines Using Genetic Algorithms

Dao¹,

Marian²

2011

CTA

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This paper presents an integrated methodology for the modelling and optimisation of precedence-constrained production sequencing and scheduling for multiple production lines based on Genetic Algorithms (GA). The problems in this class are NP-hard combinatorial problems, requiring a triple optimisation at the same time: allocation of resources to each line, production sequencing and production scheduling within each production line. They are ubiquitous to production and manufacturing environments. Due to nature of constraints, the length of solutions for the problem can be variable. To cope with this variability, new strategies for encoding chromosomes, crossover and mutation operations have been developed. Robustness of the proposed GA is demonstrated by a complex and realistic case study.

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An efficient genetic algorithm for the traveling salesman problem with precedence constraints

Cited by 208 publications

References 16 publications

Enhanced Order Crossover for Permutation Problems

Enhanced Order Crossover for Permutation Problems

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Modeling and Optimisation of Precedence-Constrained Production Sequencing and Scheduling for Multiple Production Lines Using Genetic Algorithms

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