Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

Cui, Ying; Zhong, Jiabao; Yang, Fengru; Li, Shixin; Li, Penghao

doi:10.1109/access.2020.3045765

Cited by 16 publications

(12 citation statements)

References 47 publications

(66 reference statements)

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“…Besides, it communicates with the microcontroller and adjusts the duty cycle value accordingly. Buck-boost converter is convenient for maximum power tracking purposes: It works in buck mode when the resistance load is lesser than the internal resistance of PV array at MPP, and the opposite is true for the boost mode operation [17].…”

Section: Buck-boost Convertermentioning

confidence: 99%

Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

Dagal

Akın

Akboy

2022

Intl J of Energy Research

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In this study, an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems is investigated. The effect of PV partial shading conditions, uniform and fasttracking irradiance, duty cycle, frequency, temperature changes, and load types, and besides some comparative studies of different algorithms are adequately examined for better performance study of the proposed technique. The proposed improved salp swarm algorithm based particle swarm optimization utilizes the PV Solarex-MSX-60 photovoltaic solar panel, which considers voltage and current as inputs based on the proposed algorithm parameters selection. Besides, it uses a buck-boost converter as an interface between input and output. The particle swarm optimization monitors the PV voltage and current, and the salp swarm algorithm does for the duty cycle (particles) in various environmental conditions. The proposed algorithm performs efficiencies 99.99%, 99.63%, and 99.24% comparison with other methods, under uniform irradiance and fast-tracking irradiance respectively. Moreover, the highest power of 316.32 W reached at the duty cycle of 0.6 and 428.6 W at the frequency of 30 kHz under the same partial shading condition with optimal operating temperature values 10 C

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Section: Buck-boost Convertermentioning

confidence: 99%

Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

Dagal

Akın

Akboy

2022

Intl J of Energy Research

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“…In [25], [26], [27], [28], [29], and [30], the performance of the original PSO algorithm is improved.…”

Section: Related Workmentioning

confidence: 99%

“…In [29], a multi-subdomain grouping based PSO (MS-GPSO) is proposed for solving TSP. The objective is to improve the computing efficiency of PSO while solving small and medium-sized TSP, increase the population diversity and prevent local optima.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Swap Sequence-Based Particle Swarm Optimization Algorithm to Solve TSP

et al. 2021

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The Traveling Salesman Problem (TSP) is a combinatorial optimization problem that is useful in a number of applications. Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. Several variants of PSO have been proposed for solving discrete optimization problems like TSP. Among them, the basic algorithm is the swap sequence based PSO, however, it does not perform well in providing high quality solutions. To improve the performance of the swap sequence based PSO, this paper introduces an enhanced swap sequence based PSO algorithm by integrating the strategies of the Expanded PSO (XPSO) in the swap sequence based PSO. This is because although XPSO is only suitable for solving continuous optimization problems, it has a high performance among the variants of PSO. In our work, the TSP problem is used to model a package delivery system in the Kuala Lumpur area. The problem set consists of 50 locations in Kuala Lumpur. Our aim is to find the shortest route in the delivery system by using the enhanced swap sequence based PSO. We evaluate the algorithm in terms of effectiveness and efficiency while solving TSP. To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. The proposed algorithm is found to provide better solutions with shorter paths when applied to TSP as compared to swap sequence based PSO. However, the swap sequence based PSO is found to converge faster than the proposed algorithm when applied to TSP.

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“…There are also two paragraphs of PSO in the literature that optimize different objective functions in two different stages. In addition, the path obtained in the literature is formed by connecting multiple Ferguson splines and only realizes C1 continuity; that is, the acceleration magnitude at the connection will change suddenly, and there will be an unreal feeling in the virtual scene [ 13 – 18 ]. At present, the solution process of the PSO algorithm used to solve the TSP problem is relatively complex and the solution speed is slow.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Algorithm and Its Application in Tourism Route Design and Optimization

Liu¹,

Zhou

2022

Computational Intelligence and Neuroscience

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Most of the traditional tourism route planning algorithms only consider single-factor planning, that is, the influence of scenic spots on route planning. Particle swarm optimization algorithm is favored by many people because of its simple concept, easy implementation, and good robustness. Aiming at this problem, this paper takes the actual geographic data as the research object of the tourism route problem and describes the model of the discrete particle swarm algorithm based on geographic coordinates to solve the tourism route problem, which is used to solve practical problems. In order to further improve the global search ability of the algorithm, a self-balancing mechanism is proposed, which makes the algorithm process simple and the algorithm performance improved. At the same time, multithread parallelism is adopted to improve the solution speed of the algorithm, which makes up for the deficiency of the parallelization research of the algorithm.

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Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

Cited by 16 publications

References 47 publications

Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems

An Enhanced Swap Sequence-Based Particle Swarm Optimization Algorithm to Solve TSP

Particle Swarm Algorithm and Its Application in Tourism Route Design and Optimization

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