An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

Sung, Jinmo; Jeong, Bo-Young

doi:10.1155/2014/313767

Cited by 15 publications

(14 citation statements)

References 20 publications

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“…Thus, it should be concluded from Table 3 that the proposed MBSO outperforms the SBO, MSBO I , and MSBO S . Another performance analysis for the MSBO is made by comparing the proposed algorithm with two recent meta-heuristic algorithms proposed for the PCTSP and SOP, which are Adaptive Evolutionary Algorithm (AEA) introduced by Sung and Jeong [34] and an improved Ant Colony System (ACS) introduced by Skinderowics [35]. Table 4 shows the available results of the AEA and ACS and comparisons between MBSO and other two algorithms, where "best" and "time" represent the best results and average computational time of the runs for a specified instance.…”

Section: Performance Analyses Of the Msbomentioning

confidence: 99%

“…With regards to the ACS solutions, better results are obtained for most of the instances by the MBSO. For the computational times, it is reported by the Sung and Jeong [34] that the computational time of the AEA varies between 0.01-290.93 seconds for the considered problems. For the ACS, the authors report that the results are obtained with 60 seconds time limitation [35].…”

Section: Performance Analyses Of the Msbomentioning

confidence: 99%

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Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

Küçükoğlu

Gündüz

Balkancioglu³

et al. 2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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In this study, a tool path optimization problem in Computer Numerical Control (CNC) milling machines is considered to increase the operational efficiency rates of a company. In this context, tool path optimization problem of the company is formulated based on the precedence constrained travelling salesman problem (PCTSP), where the general form of the TSP model is extended by taking the precedence of the tool operations into account. The objective of the model is to minimize total idle and unnecessary times of the tools for internal operations. To solve the considered problem, a recent optimization algorithm, called Satin Bowerbird Optimizer (SBO), is used. Since the SBO is first introduced for the global optimization problems, the original version of the SBO is modified for the PCTSP with discretization and local search procedures. In computational studies, first, the performance of the proposed algorithm is tested on a well-known PCTSP benchmark problems by comparing the proposed algorithm against two recently proposed meta-heuristic approaches. Results of the comparisons show that the proposed algorithm outperforms the other two competitive algorithms by finding better results. Then, the proposed algorithm is carried out to optimize the hole drilling processes of three different products produced by the company. For this case, with up to 4.05% improvement on the operational times was provided for the real-life problem of the company. As a consequence, it should be noted that the proposed solution approach for the tool path optimization is capable of providing considerable time reductions on the CNC internal operations for the company.

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Section: Performance Analyses Of the Msbomentioning

confidence: 99%

Section: Performance Analyses Of the Msbomentioning

confidence: 99%

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

Küçükoğlu

Gündüz

Balkancioglu³

et al. 2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

View full text Add to dashboard Cite

show abstract

“…Thus, it is crucial to explore the optimal route options for implementing periodical redistribution, which is a traveling salesman problem (TSP). TSP is a NP-complete problem and it needs to be solved by some intelligent algorithms [19][20][21]. This paper utilized the discrete particle swarm algorithm [22] to search the optimal routes.…”

Section: Optimal Routes For Regular Redistributionmentioning

confidence: 99%

Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Chen

Jiang

et al. 2015

Mathematical Problems in Engineering

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Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.

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“…The TSP is one of the most famous and widely studied problems throughout history in operations research and computer science. It has a great scientific interest, and it is used in a large number of studies [42–44]. This problem can be defined on a complete graph G = ( V , A ) where V = { v 1 , v 2 ,…, v n } is the set of vertexes which represents the nodes of the system, and A = {( v i , v j ) : v i , v j ∈ V , i ≠ j } is the set of arcs which represents the interconnection between nodes.…”

Section: Description Of the Experimentationmentioning

confidence: 99%

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

Osaba

Carballedo

Díaz

et al. 2014

The Scientific World Journal

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Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.

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An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

Cited by 15 publications

References 20 publications

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

Application of precedence constrained travelling salesman problem model for tool path optimization in CNC milling machines

Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

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