Solving a traveling salesman problem using meta-heuristics

Nejad, Anahita Sabagh; Fazekas, Gábor

doi:10.11591/ijai.v11.i1.pp41-49

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…After the PSO indicator is calculated and normalized, the SVR model is optimized for prediction. The optimal parameters for each function in ( 9)- (12), are given in Table 6, for the class 0 and 1 period indicating predictive SVR. Classes 0 and 1 denote customers who are churn or not churn.…”

Section: Svr Analysis Resultsmentioning

confidence: 99%

“…PSO is a collective behavioral intelligence stochastic population-based algorithm. PSO generates global solutions in the search space via individual particle interactions [12]. Each particle communicates information to other particles in the form of its best position and adjusts its respective velocity position based on information about the best position.…”

Section: Introductionmentioning

confidence: 99%

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An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.

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Section: Svr Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Y. Syah,

Muliono,

Akbar Siregar

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The results indicate that the basic FA, when implemented with minor parametric adjustments, outperforms ACO, SA, and GA in most scenarios. The optimization model in [11] presents an improved ant colony algorithm model based on a path segmentation strategy. The models demonstrate the capability to efficiently handle various traffic characteristics and yield superior optimization results.…”

Section: Related Workmentioning

confidence: 99%

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Pratiba,

Ridhanya,

Ridhisha

et al. 2024

EAI Endorsed Trans Energy Web

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The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.

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“…The VRP may be described as the process of finding the most efficient set of vehicle routes to improve logistic businesses' competitiveness by saving time and cost [1], [2]. Thus, research on a variety of VRP variants were performed (e.g., VRP with pick-up and delivery (VRPPD), multi-depot vehicle routing problem (MDVRP) with time windows (MD-VRPTW), multiple depot VRP (MDVRP), and capacitated VRP (CVRP), and vehicle routing problem with time windows (VRPTW) [3], [4]. The VRPTW is an NP-hard issue that involves finding the optimal route for a group of limited-capacity vehicles between a central warehouse and a number of scattered customers, all of whom must be reached within a certain timeframe the time window as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

Mortada

Yusof

2023

IJ-AI

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>An example of a combinatorial problem is the vehicle routing problem with time windows (VRPTW), which focuses on choosing routes for a limited number of vehicles to serve a group of customers in a restricted period. Meta-heuristics algorithms are successful techniques for VRPTW, and in this study, existing modified artificial bee colony (MABC) algorithm is revised to provide an improved solution. One of the drawbacks of the MABC algorithm is its inability to execute wide exploration. A new solution that is produced randomly and being swapped with best solution when the previous solution can no longer be improved is prone to be trapped in local optima. Hence, this study proposes a perturbed MABC known as pertubated (P-MABC) that addresses the problem of local optima. P-MABC deploys five types of perturbation operators where it improvises abandoned solutions by changing customers in the solution. Experimental results show that the proposed P-MABC algorithm requires fewer number of vehicles and least amount of travelled distance compared with MABC. The P-MABC algorithm can be used to improve the search process of other population algorithms and can be applied in solving VRPTW in domain applications such as food distribution.</span></p></div></div></div>

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Solving a traveling salesman problem using meta-heuristics

Cited by 5 publications

References 30 publications

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

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