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Application of Imperialist Competitive algorithm on Solving the Traveling Salesman Problem

algorithmS 2014年第2期7卷 229-242页

作者： Xu, Shuhui Wang, Yong Huang, Aiqin Shandong Univ Sch Mech Engn Jinan 250061 Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Mfg Minist Educ Jinan 250061 Peoples R China

The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.

关键词： discrete imperialist competitive algorithm traveling salesman problem 2-opt algorithm numerical experiments

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A Hybrid Recommendation Model for Tourist Using Evolutionary algorithm Combined with Local Search algorithm for Trip Planning

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SN Computer Science 2024年第6期5卷 743页

作者： Lakshmi, J.V.N. Pallavi, M.O. School of Computer Science and Application Reva University Department of MCA Acharya Institute of Technology

Recommender systems play a crucial role in assisting tourists with travel recommendations considering the customized demands is a necessary practice for improving the tourism, business, and aids significantly in decision-making process. The study proposes using a evolutionary genetic algorithm (GA) to efficiently determine the shortest tourist route within a constrained area, allowing for quick visits to multiple destinations. The goal is to find the most efficient route with minimal physical exertion. By exploring all possible routes, the GA can identify the quickest path from the starting point to the destination. The study first provides a brief overview of the enhanced GA, followed by a detailed analysis of its construction and solution. The paper introduces a novel approach to recommendation systems in the tourism industry, combining the Genetic algorithm with 2-opt algorithm for local search adaptation. The hybrid model is used to optimize the system by considering multiple criteria. Data for this study were collected through online google map resources defining a positive ideal distance matrix for 11 locations with 121 edges. Finally, the GA is further optimized by pipeline the 2-opt algorithm, and a simulation is conducted to examine the average path convergence for each individual route by customizing based on the preferences. Finally proposed model is compared against the traditional algorithms such as Heuristic and Graph network algorithms. Subsequently, the hybrid algorithm searches among destinations to recommend the best tourist trip plan to users. Selecting the shortest trip route plan will reduce the travel cost and time. This model enhances the practical value for researchers and for practical application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2-opt algorithm Evolutionary algorithm Genetic algorithm Hybrid recommendation optimization Travel planning

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Green Logistics Vehicle Path optimization Based on Hybrid Discrete Differential Evolution algorithm 32

Green Logistics Vehicle Path Optimization Based on Hybrid Di...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang, Chunmei Shen, Jingru Hao, Jie Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China

ISBN: (纸本)9781728158556

Aiming at the problem of green logistics vehicle path optimization with the smallest carbon dioxide emissions, this paper proposes the Hybrid Discrete Differential Evolution (HDDE) algorithm. For the HDDE algorithm, adopt the integer-arranged encoding method and then encode the client as initial individual. Firstly, the mutation operation aims at reflecting the direction and degree of mutation preferably, by defining addition, subtraction and multiplication operation of the differential mutation operator based on permutation method, and simultaneously bringing in incremental subsequence position transformation;secondly, the method of partial cross mapping is to avoid the illegal offspring caused by the traditional single-point crossover;finally, combined with the 2-opt local search algorithm, the discrete differential evolution algorithm is prevented this operation from the local optimal solution so as to apply and solve the path optimization problem in discrete space all the times. Compared with the original discrete differential evolution algorithm and particle swarm optimization algorithm, the experimental results indicate: this algorithm has smaller minimum, average and standard deviation, which search for the minimum value as soon as possible and hardly become local optimum, as well as the better convergence performance which shows the algorithm will achieve a smaller carbon emission path and better effect of optimization in this paper.

关键词： Hybrid discrete differential evolution Permutation mapping Part cross mapping 2-opt algorithm Green logistics

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Hybrid Particle Swarm optimization algorithm for Large-scale Travelling Salesman Problem

Hybrid Particle Swarm Optimization Algorithm for Large-scale...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Zhang, Jiangwei Xuchang Univ Int Sch Educ Xuchang 461000 Henan Peoples R China

ISBN: (纸本)9783038350125

Based on the improved enhanced self-tentative (IEST) particle swarm optimization (PSO) algorithm, 2-opt local searching algorithm is introduced in the later evolution stage. 2-opt method further strengthen the self-tentative and make up the deficiency of the IEST PSO algorithm and solve the cross solution problem. The hybrid PSO algorithm greatly increases the chances to find the better solution in the evolutionary process. Time complexity of the 2-opt method is analyzed. Based on this the proper parameters is set to solve different benchmark TSP problems, numerical simulation results show the effectiveness and efficiency of the hybrid PSO algorithm.

关键词： Particle Swarm optimization (PSO) Improved Enhanced Self-Tentative(IEST) Travelling Salesman Problem (TSP) 2-opt algorithm

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A Hybrid Approach to Plan Itinerary for Tourists 5

A Hybrid Approach to Plan Itinerary for Tourists

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5th International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Awal, Md. Abdul Rabbi, Jakaria Hossain, Sk. Imran Hashem, M. M. A. Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781509012695

With the rapid development of information, communication and transportation systems, the number of tourists is increasing all over the world. Most of the tourists are facing problems when they visit unfamiliar places. That's why it is often difficult for an individual tourist to make an optimal sightseeing itinerary plan during a tight schedule. To help tourists in this situation, a hybrid approach has been proposed in this paper to recommend an optimal itinerary plan. In this approach, K-means clustering algorithm has been used to cluster various tourist spots based on the geographic location and preferences of the tourists. Then the individual cluster is treated as a travelling salesman problem. To find the optimal route in each cluster greedy and 2-opt algorithms are applied. To switch from one cluster to the next cluster, a route is established from the last spot of one cluster to the centroid of the next cluster. Thus, all the tourist spots will be visited efficiently.

关键词： Itinerary Planning K-means Clustering algorithm Travelling Salesman Problem Greedy algorithm 2-opt algorithm

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Discrete Bat algorithm for Traveling Salesman Problem 3

Discrete Bat Algorithm for Traveling Salesman Problem

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3rd International Conference on Information Science and Control Engineering (ICISCE)

作者： Jiang, Zhao Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509025350

In this paper, we present discrete bat algorithm (DBA) for solving the Traveling Salesman Problem (TSP). In this improved bat algorithm, on the one hand, the subtraction operator of location and location, the multiplication operator of real number and location, and the addition operator of velocity and location are redefined, on the other hand, the initial population are generated by Nearest Neighbor tour construction heuristic, and 2-opt edge-exchange algorithm is introduced to perform the local search. We test series of numerical instances by using 33 benchmark instances with sizes ranging from 55 to 318 nodes from the TSPLIB, and compare DBA with Chen and Chien's method (2011), Marinakis et al.'s method (2005), and Marinakis et al.'s method (2005). The experimental results show that the percentage deviations of DBA are better than that of the three methods and within satisfaction.

关键词： Bat algorithm Traveling Salesman Problem 2-opt algorithm combinatorial optimization

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Solving the Traveling Salesman Problem Using Ant Colony Metaheuristic, A Review 16th

Solving the Traveling Salesman Problem Using Ant Colony Meta...

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16th International Conference on Hybrid Intelligent Systems (HIS) / 8th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Kefi, Sonia Rokbani, Nizar Alimi, Adel M. Univ Sfax ENIS Res Grp Intelligent Machines REGIM Lab Sfax Tunisia Univ Sousse High Inst Appl Sci & Technol Sousse Sousse Tunisia

ISBN: (纸本)9783319529417;9783319529400

This paper presents a software application allowing to solve and compare the key metaheuristic approaches for solving the Traveling Salesman Problem (TSP). The focus is based on Ant Colony optimization (ACO) and its major hybridization schema. In this work, the hybridization ACO algorithm with local search approach and the impact of parameters while solving TSP are investigated. The paper presents results of an empirical study of the solution quality over computation time for Ant System (AS), Elitist Ant System (EAS), Best-Worst Ant System (BWAS), MAX-MIN Ant System (MMAS) and Ant Colony System (ACS), five well-known ACO algorithms. In addition, this paper describes ACO approach combined with local search approach as 2-opt and 3-opt algorithms to obtain the best solution compared to ACO without local search with fixed parameters setting. The simulation experiments results show that ACO hybridized with the local search algorithm is effective for solving TSP and for avoiding the premature stagnation phenomenon of standard ACO.

关键词： Hybrid metaheuristic Ant Colony optimization AS EAS BWAS MMAS ACS Traveling Salesman Problem 2-opt algorithm 3-opt algorithm

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Using Data Envelopment Analysis and TOPSIS method for solving flexible bay structure layout

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2011年第1期6卷 49-57页

作者： Ghaseminejad, Amin Navidi, Hamidreza Bashiri, Mahdi Shahed Univ Dept Ind Engn Tehran 18151159 Iran Shahed Univ Dept Appl Math Tehran 18151159 Iran

Unlike the classic methods for solving the facility layout problem (FLP) as a single objective form, there are some approaches with multiple objectives, for example which is discussed in approaches contain less than 50 percentage of whole operating costs in an organization. During the 4 decades researchers have been proposed different criteria and methods for solving layout problem. Recently one of the approaches for solving FLP was Data Envelopment Analysis (DEA). Proposed approach use2-opt algorithm beside DEA in a iterative Process for finding the most efficient layout, Such that 2-opt algorithm in each stage with using previous stage layout, generate layout alternatives, finally we used TOPSIS method for testing DEA result by ranking the best answer of each DEA iteration answer. The proposed algorithm has been illustration by a numerical example and result show the goodness of the proposed approach.

关键词： facility layout 2-opt algorithm DEA TOPSIS flexible bay structure

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Green Logistics Vehicle Path optimization Based on Hybrid Discrete Differential Evolution algorithm

Green Logistics Vehicle Path Optimization Based on Hybrid Di...

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第32届中国控制与决策会议

作者： Chunmei Zhang Jingru Shen Jie Hao School of Electronic Information Engineering Taiyuan University of Science and Technology

Aiming at the problem of green logistics vehicle path optimization with the smallest carbon dioxide emissions,this paper proposes the Hybrid Discrete Differential Evolution（HDDE） *** the HDDE algorithm,adopt the integer-arranged encoding method and then encode the client as initial ***,the mutation operation aims at reflecting the direction and degree of mutation preferably,by defining addition,subtraction and multiplication operation of the differential mutation operator based on permutation method,and simultaneously bringing in incremental subsequence position transformation;secondly,the method of partial cross mapping is to avoid the illegal offspring caused by the traditional single-point crossover;finally,combined with the 2-opt local search algorithm,the discrete differential evolution algorithm is prevented this operation from the local optimal solution so as to apply and solve the path optimization problem in discrete space all the *** with the original discrete differential evolution algorithm and particle swarm optimization algorithm,the experimental results indicate:this algorithm has smaller minimum,average and standard deviation,which search for the minimum value as soon as possible and hardly become local optimum,as well as the better convergence performance which shows the algorithm will achieve a smaller carbon emission path and better effect of optimization in this paper.

关键词： Hybrid discrete differential evolution Permutation mapping Part cross mapping 2-opt algorithm Green logistics

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Research on Logistics Vehicle Routing optimization Based on Improved Ant Colony algorithm

Research on Logistics Vehicle Routing Optimization Based on ...

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作者： Wang Xiufan Jilin Vocational and Technical Institute Communications

Aiming at the multi-objective optimization problem in logistics distribution, in order to manage vehicle routing more reasonably, a logistics distribution path planning model based on improved ant colony optimization algorithm is proposed to minimize the distribution cost. Firstly, the logistics distribution vehicle scheduling model is analyzed, and then on the basis of ant colony algorithm, 2-opt algorithm is used to improve it. When the search falls into local optimum, the probability of random selection is increased to avoid the premature phenomenon of ant colony. Finally, an example is given to validate the model. The results show that under the same environment, compared with the traditional ant colony algorithm, the improved algorithm can optimize the distribution path accurately, has faster optimization speed, and can effectively reduce the cost of logistics distribution.

关键词： Logistics vehicle Route optimization Improved ant colony algorithm 2-opt algorithm

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