A new approach to solving the multiple traveling salesperson problem using genetic algorithms

Carter, Arthur E.; Ragsdale, Cliff T.

doi:10.1016/j.ejor.2005.04.027

Cited by 212 publications

(147 citation statements)

References 19 publications

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“…To evaluate the performance of DEABC, test problems in [8] is used to compare the computational results. The data are Euclidean, two dimensional symmetric problems, and the first city is always used as depot.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The genetic algorithm with one chromosome representation and the genetic algorithm with two chromosomes used in [8]; the two-part chromosome representation based on genetic algorithm proposed in [8] is referred to as GA2PC, the steady state grouping genetic algorithm proposed in [9] is referred to as GGA-SS; the ant colony algorithm proposed in [10] is referred to as ACO. The values of parameters are shown in the table 2, which are chosen empirically.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Table 4 and 5 compare the performance of DEABC with GA1C, GA2C, GA2PC, GGA-SS, ACO and TCX of average solution quality for objective 1 and objective2 on test problems used in [8]. The results of GA1C, GA2C, GA2PC are taken from [8], the results of GGA-SS, ACO and TCX are taken from [9], [10].…”

Section: Comparison Of Proposed Operatorsmentioning

confidence: 99%

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Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Xue

Wang

Sheng

2016

MATEC Web of Conferences

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Abstract:The double evolutional artificial bee colony algorithm (DEABC) is proposed for solving the single depot multiple traveling salesman problem (MTSP). The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-ofthe-art methods.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Xue

Wang

Sheng

2016

MATEC Web of Conferences

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“…Los aportes noveles a la resolución de este problema se han inspirado en diversos fenómenos observables en la realidad, como por ejemplo, la evolución biológica (algoritmos genéticos: CARTER; RAGSDALE, 2006;WHITLEY;HAINS;HOWE, 2010), el comportamiento de hormigas reales (DORIGO; GAMBARDELLA, 1995GAMBARDELLA, , 1997 y el de abejas (KARABOGA; GORKEMLI, 2011), entre muchos otros. Mayor información sobre ello puede encontrarse en Pérez (2011a), quien concluye que, hoy día, para mover las puntas actuales de conocimiento …”

Section: Introductionunclassified

Sacrificio cortoplacista adaptativo 2opt (SCA_2opt): Una heurística inspirada en el pensamiento sistémico

Rave¹,

Álvarez²

2013

Prod.

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Outlining two novice heuristics for the symmetric TSP, inspired systemic thinking: Sacrifice short-term adaptive 2-opt (SCA2opt) and SCA_2opt_r. These arise from the systemic analysis of the rule of decision, nearest neighbour, identifying the archetype "Counterproductive solutions". SCA relies on that agent traveler, renounces at any given time immediately to a nearby city, and moves towards the second nearest available city. From there, it continues with the nearest neighbour rule. Each is the SCA (global search) operates a local search 2opt. Where as the binomial effectiveness and efficiency, the two new heuristics are shown promising in comparison multicriteria against 19 metaheuristics. It is evident that systemic thinking is a field of inspiration viable for the development of combinatorial optimization methods; emerging questions for future developments are expressed, enabling to continue to integrate elements of the classical optimization with the systemic thought; areas traditionally seen as antagonistic, but whose dialogue is favourable in this article.

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“…Liaw et al [18] proposed a hybrid genetic algorithm, which is based on tabu search, to solve the MTSP. Carter et al [19] researched chromosome representation and related genetic operators to find an applicable method for solving the MTSP. Additionally, the ant system, which was proved by [7], is a perfectly acceptable meta-heuristic for a number of NP-hard problems.…”

mentioning

confidence: 99%

A novel method for solving the multiple traveling salesmen problem with multiple depots

Hou

Liu

2012

Chin. Sci. Bull.

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Multi-traveling salesman problem (MTSP) is an extension of traveling salesman problem, which is a famous NP hard problem, and can be used to solve many real world problems, such as railway transportation, routing and pipeline laying. In this paper, we analyze the general properties of MTSP, and find that the multiple depots and closed paths in the graph is a big issue for MTSP. Thus, a novel method is presented to solve it. We transform a complicated graph into a simplified one firstly, then an effective algorithm is proposed to solve the MTSP based on the simplified results. In addition, we also propose a method to optimize the general results by using 2-OPT. Simulation results show that our method can find the global solution for MTSP efficiently. The traveling salesman problem (TSP) is a typical combinatorial optimization problem. A generalization of the TSP is the multiple traveling salesmen problem (MTSP), which determines a set of routes enabling multiple salesmen to start at and return to depots. [17] used a competition-based neural network to solve a minmax MTSP. Thus far, GAs have been applied to a wide range of application areas, including solving the MTSP. Liaw et al. [18] proposed a hybrid genetic algorithm, which is based on tabu search, to solve the MTSP. Carter et al. [19] researched chromosome representation and related genetic operators to find an applicable method for solving the MTSP. Additionally, the ant system, which was proved by [7], is a perfectly acceptable meta-heuristic for a number of NP-hard problems. In [20], an ant system is applied to the MTSP.The MTSP seems to be more appropriate than the TSP for practical applications and can be used to simulate many everyday applications such as transportation logistics, job planning, vehicle scheduling, and so on. Some reported applications are presented in [10]. The main applications include print press scheduling [21], crew scheduling [22], school bus routing [23], mission planning [24], and the de-

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A new approach to solving the multiple traveling salesperson problem using genetic algorithms

Cited by 212 publications

References 19 publications

Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Sacrificio cortoplacista adaptativo 2opt (SCA_2opt): Una heurística inspirada en el pensamiento sistémico

A novel method for solving the multiple traveling salesmen problem with multiple depots

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