Chaotic Particle Swarm Optimization for Imprecise Combined Economic and Emission Dispatch Problem

El-Shorbagy, M. A.; Mousa, Abd Allah A.

doi:10.18488/journal.79.2017.41.20.35

Cited by 20 publications

(14 citation statements)

References 25 publications

(28 reference statements)

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“…In particular, IPMSM use magnetic torque and reluctance torque simultaneously. In addition, the control is advantageous in the field of weak field, which enables wide operation [5]- [7]. The driving curve of the IPMSM for driving the EVs shows the same characteristic as the driving curve of an ideal vehicle through about field weakening control at high-speed driving.…”

Section: Introductionmentioning

confidence: 92%

Characteristic Analysis of Electromagnetic Multi-Shift of Interior Permanent Magnet Synchronous Motor

Lim¹,

Kim²,

Kim³

2019

ujeee

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Background/ Objectives: In order to increase the travel distance of an electric vehicle, it is important to widen the efficiency range of the motor. In this paper, we study the electromagnetic multi-shift method of the motor. Methods/ Statistical analysis: We will study the expansion of the efficiency range of the motor by applying three electromagnetic multi-shift methods. In addition, the characteristics, advantages and disadvantages of methods that switch finite element analysis methods (FEMs) are analyzed. Findings: In this paper, we study the characteristic analysis of the electromagnetic multi-shift of interior permanent magnet synchronous motor (IPMSM). For electric vehicles, the Driving Range is important. Therefore, the expansion of the range of maximum efficiency is more important than the improvement of the maximum efficiency point of the motor. Three electromagnetic transmission methods were applied and analyzed to increase the efficiency range. The first method is the high efficiency range appears to be expanded in the high-speed range, but the influence of the expansion of the maximum efficiency range is not significantly expanded as compared with the other method. In addition, second method and third method in the range of maximum efficiency are similar. However, when third method is the maximum efficiency value increases up to about 8,500 rpm operation range at about 5,000 rpm. Improvements/Applications: The efficiency of the motor to which various electromagnetic multi-shift methods were applied was derived by FEM and the characteristics were analyzed.

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Section: Introductionmentioning

confidence: 92%

Characteristic Analysis of Electromagnetic Multi-Shift of Interior Permanent Magnet Synchronous Motor

Lim¹,

Kim²,

Kim³

2019

ujeee

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“…SI is related to the study of swarms, or colonies of social organisms; where studies of the social behavior in swarms of organisms inspired the design of many efficient optimization techniques. For example, the simulation of bird flocks resulted in the particle swarm optimization (PSO) algorithm [8][9][10][11], and the studies the behavior of ants led to the design of the ant colony optimization (ACO) algorithm [12,13].…”

Section: Introductionmentioning

confidence: 99%

Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

El-Shorbagy¹,

Ayoub²

2021

IJCIS

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This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. In addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies; where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem.

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“…Until now, many PBAs have been proposed in the literature and have been successfully applied to solve optimization problems. Examples of PBAs models are: genetic algorithm (GA) [11,12], ant colony optimization (ACO) [13], particle swarm optimization (PSO) [14][15][16], artificial bee colony (ABC) [17], bacterial foraging (BF) [18], cat swarm optimization (CSO) [19], glowworm swarm optimization (GSO) [20], firefly algorithm (FA) [21], monkey algorithm (MA) [22], krill herd algorithm (KHA) [23], sine cosine algorithm (SCA) [24] and grasshopper optimization algorithm (GOA) [25], cuckoo search algorithm (CSA) [26], salp swarm algorithm (SSA) [27], gradientbased optimizer (GBO) [28], slime mould algorithm (SMA) [29], and harris hawks optimization (HHO) [30], etc.…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

El-Shorbagy

El-Refaey

2020

IEEE Access

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Chaotic Particle Swarm Optimization for Imprecise Combined Economic and Emission Dispatch Problem

Cited by 20 publications

References 25 publications

Characteristic Analysis of Electromagnetic Multi-Shift of Interior Permanent Magnet Synchronous Motor

Characteristic Analysis of Electromagnetic Multi-Shift of Interior Permanent Magnet Synchronous Motor

Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

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