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

Emambocus, Bibi Aamirah Shafaa; Jasser, Muhammed Basheer; Hamzah, Muzaffar; Amphawan, Angela

doi:10.1109/access.2021.3133493

Cited by 27 publications

(33 citation statements)

References 28 publications

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“…For example, they can be used in water resources engineering [8], in wireless networks [9], in cloud-based Internet of Things [10], in optical systems [11], in recommender systems [12], in anomaly detection systems [13], and in supply chain management [14]. They can also be used for clustering [15], for feature selection [16] and for solving the traveling salesman problem [17], [18]. Moreover, they have several applications in optimal designs, electrical engineering, networking, mechanical engineering, machine learning, resource allocation, and digital image processing [19].…”

Section: B Swarm Intelligence Algorithmsmentioning

confidence: 99%

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.INDEX TERMS Artificial neural network, swarm intelligence, optimization. I. INTRODUCTIONArtificial Neural Networks (ANNs) are computational models that simulate the biological neural network that constitutes the human brain to generate inferences based on certain given information. They are suitable for both supervised and unsupervised learning for solving a myriad of classification, regression, clustering, and association problems in a multitude of areas. Notably, ANN has been a prominent algorithm in the domain of machine learning, and has paved the way for the advancement in multiple areas such as natural language processing, fraud detection, computational biology, computer vision, unassisted control of vehicles, speech recognition, medical diagnosis and recommendation systems [1]. Recently, ANNs have been applied for making decisions in healthcare organizations [2], for forecasting the energy use in buildings [3], for the development of greenhouse technol-The associate editor coordinating the review of this manuscript and approving it for publication was Yeliz Karaca .

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Section: B Swarm Intelligence Algorithmsmentioning

confidence: 99%

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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“…(5) The peak-to-peak inductor ripple current is deduced as following (6) The input current and output current relationship (7) The maximum peak-to-peak inductor ripple current (8)…”

Section: The Input Voltage and Output Voltage Relationship With The D...mentioning

confidence: 99%

“…In complete radiation variation conditions, the traditional algorithms effectively track the MPP of the PV array, but they did not succeed to track the MPP under partial shading or varying environmental conditions. The design [6] is appropriate for solving uninterrupted optimization problems with high performance among the PSO variants.…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm optimization based on buck-boost converter (IPSO-BBC) for photovoltaic system applications

AKIN¹

2022

Recent Adv Sci Eng

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Nowadays, energy access and demands are of high importance. In some remote areas where the main energy grids are difficult to access, photovoltaic solar standalone systems are highly recommended for utility operation. In order to achieve and maintain successfully the required output power at maximum levels under any variable environmental conditions, high PV solar powers are necessary to feed efficiently the output. In this paper, an improved particle swarm optimization-based buck-boost converter (IPSO-BBC) for optimum standalone load charging is investigated. The proposed IPSO-BBC algorithm utilizes the PV solar voltage and current as two inputs based on PSO parameters. Solarex-MSX photovoltaic solar panel of 60 watts maximum power is used for the whole system operation. In order to harvest the maximum power for the output satisfaction, therefore; for this purpose, the IPSO-BBC algorithm approach is implemented. The PSO is used to monitor the PV voltage and current and BBC for the duty cycle settlement in various environmental conditions. The IPSO-BBC approach optimally presents a very good efficiency of 98.24% and a tracking factor of 0.93 with PV solar power generation performance under partial shading conditions. The entire simulation is carried out by MATLAB/Simulink.

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“…Ref. 22 presents an enhanced swap sequence‐based PSO (Enhanced SSPSO) to improve the performance of the salp chain movement. A fully adaptive PSO algorithm for determining the global power of multi‐modal P‐V curves in PSCs was studied in Ref.…”

Section: Introductionmentioning

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

Cited by 27 publications

References 28 publications

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

Improved particle swarm optimization based on buck-boost converter (IPSO-BBC) for photovoltaic system applications

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

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