Particle Swarm Optimization Algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

Lee, Jin Hwan; Song, Jun-Young; Kim, Dae Woo; Kim, Jong-Wook; Kim, Yong-Jae

doi:10.1109/tie.2017.2760838

Cited by 111 publications

(27 citation statements)

References 22 publications

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“…The PSO algorithm which is a meta heuristic algorithm based on the social behaviour of birds striving to achieve a goal was developed by Kennedy and Eberhart [11,35]. PSO algorithm has been used in different fields such as industry engineering [36], civil engineering [37], energy systems engineering [38], electrical engineering [39] and geology engineering [40] because of its successful performance. Qu and Lou [41] used the PSO algorithm for the optimal allocation of regional water resources.…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

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Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.

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Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

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“…Kazerroni et al [24] used a fuzzy adaptive PSO technique for optimal design of slotless, brushless PM machines with a 2D analytical model. In [25], a modified PSO algorithm was applied for optimal design of an interior PMSM to minimize the total harmonic distortion (THD) of the back EMF. Inspired by the behavior of swarms of insects, PSO optimizes a problem through an iterative loop moving the candidate solution (particles) toward the best solution with regard to predefined measures of fitness.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Particle swarm optimization approach to optimal design of an AFPM tractionmachine for different driving conditions

Rostami¹

2019

Turk J Elec Eng & Comp Sci

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Axial flux permanent magnet (AFPM) machines can be employed as the traction motor of electric vehicles due to their high torque capability, high efficiency, modular and compact construction, and capability of integration with other mechanical components in integrated systems. Besides, the system efficiency can be further improved by optimal design of the selected electric machine. In this paper, an AFPM machine is optimized against two well-known driving cycles called the New European Drive Cycle (NEDC) and US06 and the influence of the driving cycle on the obtained machine parameters is evaluated. US06 is the more demanding driving cycle and thus the machine designed for this driving cycle demands more electrical loading compared to the machine designed for NEDC. Therefore, the copper loss minimization becomes more important for the US06-optimized machine compared to the NEDC-optimized machine.Consequently, the machine design parameters optimized for different driving cycles would be quite different. Compared to the NEDC-optimized machine, the US06-optimized machine has a lower number of coil turns, lower height of teeth, and lower diameter ratio to limit the copper losses. Furthermore, fewer magnets are needed for the motor optimized for the NEDC compared to the motor optimized for US06. A quasi-3D approach and particle swarm optimization algorithm are used in the semianalytical design optimization process. Additionally, computationally efficient 3D finite-element analysis and measurements made for the prototype AFPM machine are carried out to validate the accuracy of the quasi-3D approach.

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“…Particle swarm optimization as one of outstanding swarm intelligent algorithms has been widely used to solve enormous real-word problems, such as optimal design of electric machines problem [47], Wi-Fi indoor positioning problem [14], indoor high precision three-dimensional positioning problem [10], energy management problem [57], economic load dispatch problems [61], just to name a few. It is no exaggeration to say that PSO has unique performance nearly in every area, like industrial engineering, intelligent manufacture, data mining, information and communication system, automatic control system, image processing.…”

Section: Real-world Applicationsmentioning

confidence: 99%

A quarter century of particle swarm optimization

Cheng

Lei

et al. 2018

Complex Intell. Syst.

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Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, the particle swarm optimization algorithm has attracted many researchers' attention. Through the convergent operation and divergent operation, individuals in PSO group and diverge in the search space/objective space. In this paper, the historical development, the state-of-the-art, and the applications of the PSO algorithms are reviewed. In addition, the characteristics and issues of the PSO algorithm are also discussed from the evolution and learning perspectives. Every individual in the PSO algorithm learns from itself and another particle with good fitness value. The search performance and convergence speed were affected by different learning strategies. The scheduling and data-mining problems are illustrated as two typical cases of PSO algorithm solving real-world application problems. With the analysis of different evolution and learning strategies, particle swarm optimization algorithm could be utilized on solving more real-world application problems effectively, and the strength and limitation of various PSO algorithms could be revealed.

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Particle Swarm Optimization Algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

Cited by 111 publications

References 22 publications

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Particle swarm optimization approach to optimal design of an AFPM tractionmachine for different driving conditions

A quarter century of particle swarm optimization

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