A Novel Competitive Particle Swarm Optimization Algorithm Based on Levy Flight

Yao, Ning; Liu, Zhongxin; Chen, Zengqiang; Zhao, Chen

doi:10.1007/978-981-32-9682-4_58

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…As shown in Figure 3, PSO falls into local optimal and cannot fly out of the local optimal area, it is unable to find the global optimal. However, the algorithm in this paper can jump out of the local optimal region by using Levy flight when it is falled in the local minimum [6] . The global search capability is improved and the global optimal is found.…”

Section: Experiments On Global Optimization Abilitymentioning

confidence: 99%

Particle Swarm Optimization Algorithm Based on Levy Flight

Su,

Xue,

Wen

2023

Frontiers in Artificial Intelligence and Applications

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On account of the shortcomings of particle swarm optimization algorithm(PSO), such as poor global search capability and low convergence accuracy, levy flight [1] and Gaussian mutation [2] are used to propose particle swarm optimization algorithm based on levy flight. In iteration, the particle aggregation degree [3] is calculated, and the corresponding probability is selected to carry out levy flight [4] according to the particle aggregation degree, it strengthen the global optimization ability. Gaussian variation is carried out on particle positions of each iteration, and particles with better fitness are selected for iteration, which enhances the local search capability. At the same time, adaptive perturbation is carried out to the global optimal position of each iteration to increase the optimization precision of the optimal value. The test shows that the convergence rate of the improved algorithm is better than that of PSO, and the convergence accuracy is higher.

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Section: Experiments On Global Optimization Abilitymentioning

confidence: 99%

Particle Swarm Optimization Algorithm Based on Levy Flight

Su,

Xue,

Wen

2023

Frontiers in Artificial Intelligence and Applications

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show abstract

“…Levy flight which simulates the food searching path of numerous animals like deer, bumblebees, and albatross are added to SI algorithms to promote the performance of the algorithms such as PSO [ 13 ], cuckoo search algorithm (CSA) [ 14 ] and firefly algorithm (FA) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Bacterial Foraging Optimization Based on Levy Flight for Fuzzy Portfolio Optimization

Zhou

Qiu

2020

Lecture Notes in Computer Science

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In this paper, a new kind of bacterial foraging optimization that combines with levy flight (LBFO) is employed to solve a novel portfolio optimization (PO) problem with fuzzy variables and modified mean-semivariance model which includes the transaction fee (including the purchase fee and sell fee), no short sales and the original proportion of the different assets. First of all, a chemotaxis step size using levy distribution takes the place of fixed chemotaxis step size, which makes a good balance between local search and global search through frequent short-distance search and occasional long-distance search. Moreover, fuzzy variables are used to signify the uncertainty of future risks and returns on assets and some constrained conditions are taken into consideration. The results of the simulation show that the model can be solved more reasonably and effectively by LBFO algorithm than the original bacterial foraging optimization (BFO).

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Improved Ant Lion Algorithm for Solution of Optimal Power Flow

Saini

Rahi

2022

2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC)

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A Novel Competitive Particle Swarm Optimization Algorithm Based on Levy Flight

Cited by 3 publications

References 19 publications

Particle Swarm Optimization Algorithm Based on Levy Flight

Particle Swarm Optimization Algorithm Based on Levy Flight

Bacterial Foraging Optimization Based on Levy Flight for Fuzzy Portfolio Optimization

Improved Ant Lion Algorithm for Solution of Optimal Power Flow

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