Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization

Zhang, Xinming; Lin, Qiuying; Mao, Wentao; Liu, Shangwang; Dou, Zhi; Liu, Guoqi

doi:10.1016/j.asoc.2020.107061

Cited by 94 publications

(33 citation statements)

References 46 publications

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“…The hunting cycle in the GWO commences with the acquisition of a random population of candidate solutions (wolves) followed by identifying optimal prey's locations using a cyclic process. GWO has several advantages compared with evolutionary approaches, easy programming and implementation, algorithm simplicity, no need for algorithm-specific parameters, and lower computational complexity [110]. In recent years, GWO has been increasingly used in diverse disciplines.…”

Section: Gray Wolf Optimizer (Gwo)mentioning

confidence: 99%

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Jamal¹,

Al-Ahmadi²,

Butt³

et al. 2023

Search Algorithm - Essence of Optimization

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Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review.

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Section: Gray Wolf Optimizer (Gwo)mentioning

confidence: 99%

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Jamal¹,

Al-Ahmadi²,

Butt³

et al. 2023

Search Algorithm - Essence of Optimization

View full text Add to dashboard Cite

show abstract

“…The average overall accuracy (OA) and the average number of selected bands (NB) on the three HSIs are presented in Tables 2-4. In addition, we performed the Wilcoxon signed-rank test [53] on the experimental records, a commonly used non-parametric statistical hypothesis test. The original hypothesis was that there would be no difference between the fitness values of each F-W-SIEA and the corresponding pure SIEA.…”

Section: Accuracy and Number Of Selected Bandsmentioning

confidence: 99%

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

Shang

Zheng

et al. 2022

Remote Sensing

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Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter–wrapper (F–W) framework that can improve the SIEAs’ performance; and (2) to apply ten SIEAs under the F–W framework (F–W–SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs’ performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F–W–Genetic Algorithm (F–W–GA) and F–W–Grey Wolf Optimizer (F–W–GWO) have the strongest optimization abilities, while the F–W–GWO requires the least runtime among the ten. The F–W–Marine Predators Algorithm (F–W–MPA) is second only to the two and slightly better than F–W–Differential Evolution (F–W–DE). The F–W–Ant Lion Optimizer (F–W–ALO), F–W–I-Ching Divination Evolutionary Algorithm (F–W–IDEA), and F–W–Whale Optimization Algorithm (F–W–WOA) have the middle optimization abilities, and F–W–IDEA takes the most runtime. Moreover, the F–W–SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.

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“…In the past few years, algorithms such as Cuckoo Search have been used too [24,25,26]. A hybrid approach using PSO and Grey Wolf Optimization was proposed by Xinming Zhang et al [27]. PSO has also been the subject of focus for many Genome Sequence Assembly papers [28,29,30].…”

Section: Consensus Stagementioning

confidence: 99%

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Jain¹,

Bharti²

2021

Preprint

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With the advent of Genome Sequencing, the field of Personalized Medicine has been revolutionized. From drug testing and studying diseases and mutations to clan genomics, studying the genome is required. However, genome sequence assembly is a very complex combinatorial optimization problem of computational biology. PSO is a popular metaheuristic swarm intelligence optimization algorithm, used to solve combinatorial optimization problems. In this paper, we propose a new variant of PSO to address this permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of the PSO proposed in the literature. The analysis is conducted on four DNA coverage datasets. The conducted analysis demonstrates that the proposed model attain a better performance with better reliability and consistency in comparison to others competitive methods in all cases.

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Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization

Cited by 94 publications

References 46 publications

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

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