A Learning Sparrow Search Algorithm

Ouyang, Cheng; Zhu, Donglin; Wang, Fengqi

doi:10.1155/2021/3946958

Cited by 71 publications

(36 citation statements)

References 26 publications

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“…A learning SSA (LSSA) [ 65 ] is introduced in the discoverer stage. The random reverse learning technique promotes population variety and flexibility in the search process.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

191

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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“…A learning SSA (LSSA) [ 65 ] is introduced in the discoverer stage. The random reverse learning technique promotes population variety and flexibility in the search process.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

191

View full text Add to dashboard Cite

show abstract

“…Literature [ 27 ] makes full use of the current dominant individuals through the iterative local search mechanism, making the search method more diversified and the optimization accuracy more detailed. Literature [ 28 ] introduced the dimension-by-dimension lens learning mechanism to reduce the interference between dimensions and accelerate the convergence of the population. Inspired by logistic model, literature [ 29 ] proposed a new adaptive factor to dynamically control the safety threshold, which balances the ability of global search and local development of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism

Yang

Yan

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields.

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“…Then, a differential evolution approach was also presented to avoid missing high-quality solutions to enhance the ability to escape local optima. Finally, the algorithm's feasibility was verified by comparing three SSA variants and three classical SI algorithms [13]. Wang et al used Bernoulli chaos mapping and adaptive weighting factors to improve the global search range of the SSA and used a hybrid Cauchy mutation and reverse learning to jump out of the local optimum.…”

Section: Introductionmentioning

confidence: 99%

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Zhang

Zhou

et al. 2022

IEEE Access

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Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.INDEX TERMS Sparrow search algorithm, cubic chaos mapping, adaptive weighting modification, levy flight, reverse learning.

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A Learning Sparrow Search Algorithm

Cited by 71 publications

References 26 publications

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

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