Modeling and optimization of multiple traveling salesmen problems: An evolution strategy approach

Karabulut, Korhan; Öztop, Hande; Kandiller, Levent; Taşgetiren, M. Fatih

doi:10.1016/j.cor.2020.105192

Cited by 26 publications

(22 citation statements)

References 43 publications

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“…Each TSP instance is solved 10 times by ITSHA in the comparison, each time with a different random seed. The cut-off time of ITSHA is set to seconds as GVNS [12] and ES [14] do.…”

Section: Methodsmentioning

confidence: 99%

“…end for Connect city and in the solution 27: end for tics [12,20,14] are inefficient. To handle this problem, we introduce three neighborhoods, 2-opt, Insert and Swap, applied in our ITSHA algorithm that are significantly more effective and efficient than the neighborhoods used in [12,20,14]. The three neighborhoods are illustrated in Figure 1.…”

Section: Neighborhoods Used In Itshamentioning

confidence: 99%

“…Heuristics are known to be the most efficient and effective approaches for solving the TSP. For representative and effective heuristic algorithms, we have the artificial bee colony (ABC) algorithms [11], the invasive weed optimization (IWO) algorithm [11], the general variable neighborhood search (GVNS) heuristic [12], the ant colony optimization (ACO) algorithm [13] and the evolution strategy (ES) approach [14]. These heuristics can be used to solve both of the minsum and the minmax TSP.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the genetic algorithms (GA) [15,16,17], ABC algorithms [11], ACO algorithms [13,18], evolution strategy algorithm [14], and other population based approaches [11]. Among these algorithms, the ABC algorithms as well as the IWO algorithm proposed by Venkatesh and Singh [11], the MOAT-ACO algorithm [13], and the ES algorithm [14] are the state-of-the-art heuristics for both of the minsum and the minmax TSP. In addition, some studies focus on one of the two objectives of TSP.…”

Section: Introductionmentioning

confidence: 99%

“…Local search is another type of heuristic algorithms, which is widely used in the famous combinatorial optimization problems such as TSP [21,22], satisfiability [23] and maximum satisfiability [24]. However, the local search technique is mainly applied to improve the population based TSP metaheuristics by incorporating with them in the related studies [11,20,14], and few researches employ the local search method to directly solve the standard minsum and minmax TSP. Among the local search algorithms, the GVNS algorithm proposed by Soylu [12] is one of the best-performing heuristics based on variable neighborhood search (VNS) for the minsum and minmax TSP.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Iterated Two-stage Heuristic Algorithm for the Multiple Traveling Salesmen Problem

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Hong²,

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et al. 2022

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“…Each TSP instance is solved 10 times by ITSHA in the comparison, each time with a different random seed. The cut-off time of ITSHA is set to seconds as GVNS [12] and ES [14] do.…”

Section: Methodsmentioning

confidence: 99%

Section: Neighborhoods Used In Itshamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Effective Iterated Two-stage Heuristic Algorithm for the Multiple Traveling Salesmen Problem

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Photocatalysis of low-density polyethylene using FKMW-ZnO NPs: optimization and predication model using a radial basis function neural network ensemble system

Noman,

Al-Gheethi,

Alzaeemi

et al. 2024

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The present study aimed to investigate the efficiency of biosynthesized zinc oxide nanoparticles in fungal supernatant grown in kitchen wastewater with microelectronic sludge (FKMW-ZnO NPs) to be used in the degradation low-density polyethylene (LDPE) in aqueous solution. The photocatalysis process was optimized using response surface methodology as a function of four independent factors included LDPE concentrations $$\left( {x_{1} } \right)$$ x 1 (100–500 mg/100 mL), FKMW-ZnO NPs concentrations $$\left( {x_{2} } \right)$$ x 2 (10–100 mg/100 mL), time $$\left( {x_{3} } \right)$$ x 3 (1–6 h) and pH $$\left( {x_{4} } \right)$$ x 4 (4–9). The maximum photocatalysis of LDPE was 45.43% optimized with 229.96 mg LDPE/100 mL, 100 mg FKMW-ZnO NPs/100 mL at pH 7 and after one hour with R2 is 0.7377. Microstructure and chemical structure analysis showed a significant change in the chemical structure of the photocatalysis of LDPE because of FKMW-ZnO NPs. The mathematical predication model using a radial basis function neural network ensemble system (RBFNNES) provided more accurate prediction model 89.2857% with R2 = 0.8688. However, RBFNNES revealed that FKMW-ZnO NPs and LDPE have unstable behaviour towards the investigated factor and the interaction between these factors where the error was increasing with the increasing the time of neural network which indicates that the obtained efficiency in the optimization study might be not applicable in the large scales or in different environmental factors. More optimization with a wide range of factors is required to understand the applicability of FKMW-ZnO NPs in remediation of LDPE in the environment. Graphical Abstract

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Modeling and optimization of multiple traveling salesmen problems: An evolution strategy approach

Cited by 26 publications

References 43 publications

An Effective Iterated Two-stage Heuristic Algorithm for the Multiple Traveling Salesmen Problem

An Effective Iterated Two-stage Heuristic Algorithm for the Multiple Traveling Salesmen Problem

Constrained Multi-agent Path Planning Problem

Photocatalysis of low-density polyethylene using FKMW-ZnO NPs: optimization and predication model using a radial basis function neural network ensemble system

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