Lens Learning Sparrow Search Algorithm

Ouyang, Cheng; Zhu, Donglin; Qiu, Yaxian

doi:10.1155/2021/9935090

Cited by 38 publications

(19 citation statements)

References 13 publications

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“…14 standard test functions proposed in the previous section are utilized to test the performance of the improved IHSSA. Nine intelligent optimization algorithms, including particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), teaching and learning algorithm (TLBO), Sparrow Search Algorithm (SSA), Chaos Sparrow Optimization Algorithm (CSSA) proposed by Lv et al [ 2 ], LSSA improved by Zhu DL [ 33 ], GSSA improved by Chen G, and YSSA proposed by Yan et al [ 34 ] , are chosen for comparison. In order to ensure the objectivity of the experiment and the fairness of comparison, the population size and maximum iteration number of each algorithm are 100 and 500, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

Wang

Huang

Zhu

2022

Computational Intelligence and Neuroscience

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The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

Wang

Huang

Zhu

2022

Computational Intelligence and Neuroscience

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“…As a novel technique in the area of smart computing, lens opposition-based learning (LOBL), incorporating traditional opposition-based learning (OBL) [58] and convex lens imaging discipline, has been successfully employed in different intelligent algorithm optimizations [59,60]. Its basic ideology is to simultaneously calculate and compare the candidate solution and corresponding reverse solution, and then choose the superior one to proceed with the next iteration.…”

Section: Lens Opposition-based Learningmentioning

confidence: 99%

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Xiao¹,

Sun²,

Guo³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges. Besides, a novel local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final solution precision. Attributed to three improvements, the exploration and exploitation capabilities of the basic GTO are greatly enhanced. The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions. The numerical and statistical results demonstrate that IGTO can provide better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms. Moreover, the applicability of IGTO is further proved through resolving four engineering design problems and training multilayer perceptron. The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

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“…The MATLAB-based source code for implementing this algorithm is available for registered users at https://www.mathworks.com/matlabcentral/ fileexchange/88788 (accessed on 1 December 2021). However, in [66,94], the authors proposed a variable spiral search technique for the followers to update their positions better. The position update equation of the followers using this search strategy is as follows:…”

Section: Sparrow Search Optimization Algorithmmentioning

confidence: 99%

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

et al. 2022

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This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives.

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Lens Learning Sparrow Search Algorithm

Cited by 38 publications

References 13 publications

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

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