A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

Cui, Ligang; Wang, Lin; Deng, Jie; Zhang, Jinlong

doi:10.1155/2013/159495

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…Based on the QEA with a quantum rotation gate strategy, an adaptive evolution-based quantum-inspired evolutionary algorithm (AEQEA) introduces an adaptive evolution mechanism [14]. A new improved quantum evolution algorithm (IQEA) with a mixed local search procedure was proposed [15]. Li et al [16] proposed a quantum ant colony algorithm (QACA) that combined quantum computing and the ant colony algorithm for continuous space optimization.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Xia

Han

Zhao

et al. 2019

Mathematical Problems in Engineering

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As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Xia

Han

Zhao

et al. 2019

Mathematical Problems in Engineering

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“…Simulation results demonstrate the effectiveness of the QEA. Cui et al [17] propose a new improved quantum evolution algorithm (IQEA) with a mixed local search procedure for solving the capacitated vehicle routing problem (CVRP). First, they construct an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum NOT gate to initialize and generate feasible solutions.…”

Section: The Application Of Qea In the Problem Of Distributionmentioning

confidence: 99%

A Study on the Optimization of Chain Supermarkets’ Distribution Route Based on the Quantum‐Inspired Evolutionary Algorithm

2017

Mathematical Problems in Engineering

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The chain supermarket has become a major part of China’s retail industry, and the optimization of chain supermarkets’ distribution route is an important issue that needs to be considered for the distribution center, because for a chain supermarket it affects the logistics cost and the competition in the market directly. In this paper, analyzing the current distribution situation of chain supermarkets both at home and abroad and studying the quantum-inspired evolutionary algorithm (QEA), we set up the mathematical model of chain supermarkets’ distribution route and solve the optimized distribution route throughout QEA. At last, we take Hongqi Chain Supermarket in Chengdu as an example to perform the experiment and compare QEA with the genetic algorithm (GA) in the fields of the convergence, the optimal solution, the search ability, and so on. The experiment results show that the distribution route optimized by QEA behaves better than that by GA, and QEA has stronger global search ability for both a small-scale chain supermarket and a large-scale chain supermarket. Moreover, the success rate of QEA in searching routes is higher than that of GA.

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“…Other intelligent algorithms, such as genetic-simulated annealing algorithm [46] and quantum evolution algorithm [47], also show good performances to solve complex optimization problems. In the future, we will design hybrid algorithms by taking the advantages of the above algorithms to handle more complex fuzzy JRD problems.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method

Zeng

Wang

et al. 2014

Abstract and Applied Analysis

Self Cite

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In reality, decision-makers are always in front of imprecise and vague operational conditions. We propose a practical multiobjective joint replenishment and delivery scheduling (JRD) model with deterministic demand and fuzzy cost. This model minimizes the total cost defuzzified by the signed distance method and maximizes the credibility that the total cost does not exceed the budget level. Then, an inverse weight fuzzy nonlinear programming (IWFNLP) method is adopted to formulate the proposed model. This method embeds the idea of inverse weights into the Max-Min fuzzy model. Thirdly, the fuzzy simulation approach and differential evolution algorithm (DE) are utilized to solve this problem. Results show that solutions derived from the IWFNLP method satisfy the decision-maker’s desirable achievement level of the cost objective and credibility objective. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component. Our study also shows that the improved DE outperforms DE with a faster convergence speed.

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A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

Cited by 14 publications

References 36 publications

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

A Study on the Optimization of Chain Supermarkets’ Distribution Route Based on the Quantum‐Inspired Evolutionary Algorithm

Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method

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