An Improved Sparrow Search Algorithm

Wei, Song; Liu, Song; Wang, Xiaochun; Wu, Weiguo

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00093

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…Using ( 2) or ( 12) to update the scroungers' position; (10) end for (11) for i � 1: SD do (12) Using equation ( 3) to update the scouters' position; (13) end for (14) for i � 1: N do (15) if the new position is better than the previous position then (16) Using the new position to update the previous position; (17) end if (18) if the new position is better than the optimal position then (19) Using the new position to update the optimal position; (20) end if (21) end for (22) t � t + 1 (23) end while (24) return f best , X best ALGORITHM 1: Te improved sparrow search algorithm. Computational Intelligence and Neuroscience Equation ( 13) can be expressed as a matrix as follows:…”

Section: Inputmentioning

confidence: 99%

“…However, this method is stochastic and does not make full use of the information carried by high-quality individuals in the initial population. Song et al [19] introduce nonlinear decreasing weight to improve the ability of global exploration and local exploitation, but this method cannot improve the ability to jump out of the local optimal solution. Zhang et al [20] combine the sine cosine algorithm with SSA to help SSA jump out of the local optimal solution, but this method is stochastic, and if the solution space is not well selected, it still cannot jump out of the local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Yang

Tao

Zhou

et al. 2023

Computational Intelligence and Neuroscience

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The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.

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Section: Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Yang

Tao

Zhou

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Based on this, a method combining the global search ability algorithms such as sine-cosine algorithm [62], bird swarm algorithm [63], firefly algorithm [53], and differential evolution algorithm [64] with the discoverers in SSA is proposed, which improves the global search ability of SSA with the excellent global search ability of other algorithms, "using other spears, strengthening our spears." At the same time, another adaptive improvement strategy based on balanced 12 Wireless Communications and Mobile Computing exploratory development capability is introduced into SSA, such as nonlinear inertial weight [53,65], adaptive distribution [66], and adaptive control step [67], to increase the global search capability by enhancing the previous exploratory capability. The above improvements are mainly aimed at the discoverers who occupy a minority of the population and are responsible for guiding the direction of the population.…”

Section: Biological Characteristicsmentioning

confidence: 99%

Comparative Study and Improvement Analysis of Sparrow Search Algorithm

Yan

Liu

et al. 2022

Wireless Communications and Mobile Computing

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To solve the problem that the emerging sparrow search algorithm (SSA) lacks systematic comparison and analysis with other classical algorithms, this paper first introduces the principle of the sparrow search algorithm and then describes the mathematical model and algorithm description of the sparrow search algorithm. By comparing several classical intelligent algorithms with particle swarm optimization (PSO), differential evolution (DE), and gray wolf optimizer (GWO), the sparrow search algorithm’s theory and model are systematically compared and analyzed, and the advantages and disadvantages of SSA are summarized. Finally, based on the above research and previous research, the limitations of SSA and current improved SSA are analyzed, which provides ideas for further improvement of the algorithm.

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“…J. Dong proposed a niche multi-objective SSA algorithm, introduced Lévy flight interference tactics to upgrade the capability of the SSA algorithm to skip away from the regional optimal, and used the algorithm to optimize the distributed power capacity configuration [35]. W. Song utilized a chaotic tent map to initialize people and adjusted the population using a nonlinear decreasing weight, a mutation strategy, and a chaotic search method to improve the population quality and expand the search range, thus enhancing the aptitude of the algorithm to skip away from the regional optimal [36]. G. Liu introduced an improved SSA into the UAV path-planning problem and used the global and local probe and development capabilities of the algorithm to enhance the speed and precision of path planning [37].…”

Section: Introductionmentioning

confidence: 99%

Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm

Qiao

Dou

et al. 2022

Energies

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The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer’s optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm’s skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

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An Improved Sparrow Search Algorithm

Cited by 27 publications

References 20 publications

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Comparative Study and Improvement Analysis of Sparrow Search Algorithm

Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm

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