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

Yang, Yihao; Tao, Jinxu; Zhou, Jiayu; Wang, Jiaqi; Guo, Xinyu

doi:10.1155/2023/1228685

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Compared with traditional exact algorithms, intelligent colony algorithms have characteristics of simple structure, easy realization and high efficiency, providing reliable solutions to complex optimisation problems. With the introduction of the first intelligent colony algorithm, the ant colony algorithm [1], more and more intelligent colony algorithms have emerged, such as Whale optimization algorithm (WOA) [2], Harris hawk optimization (HHO) [3], Sparrow search algorithm (SSA) [4], Subtractive Optimization-based Extreme Learning Machine (SABO) [5], Reptile search algorithm (RSA) [6], Bacterial Foraging Optimisation Algorithm (BFOA) [7], Cuckoo search algorithm (CS) [8], which have been widely used in wind power prediction [9], microgrid optimisation [10], and other industrial fields. Reference [11] applies Whale optimization algorithm in microgrid multi-objective optimal scheduling problem, which effectively reduces the cost of microgrid.…”

Section: Introductionmentioning

confidence: 99%

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

Gu,

Wang

2024

Preprint

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In order to solve the shortcomings of Dung Beetle Optimizer such as low convergence accuracy and easy to fall into local optimum, a multi-strategy improved Dung Beetle Optimizer (IDBO) is proposed. Firstly, the Cubic chaotic mapping strategy is introduced to improve the diversity of the initial population. Secondly, the global exploration strategy in the Fishhawk algorithm is introduced to give the dung beetle algorithm the exploration ability of identifying the optimal region and escaping from the local optimum, which initially improves the convergence speed and optimality-seeking accuracy of the algorithm. Finally, the dung beetle foraging behaviour is perturbed using the adaptive t-distribution perturbation strategy, making the dung beetle algorithm improve the global exploitation ability and local exploration ability while further accelerate its speed of convergence. The effectiveness of the three improved strategies is verified by testing and analysing the CEC2021 and CEC2017 test functions. The optimization results of the improved algorithms and the comparison algorithms are subjected to convergence analysis and Wilcoxon rank sum test, which proves that the IDBO algorithm has good convergence speed and optimization accuracy. In addition, the IDBO algorithm is adopted to optimise parameters of the HKELM prediction model which is applied to the short-term PV power prediction simulation and comparison experiments. Experimental results show that the IDBO-HKELM prediction model can effectively improve the prediction accuracy of short-term PV power, which further verifies the feasibility and validity of the IDBO algorithm in solving the problems of practical applications.

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Section: Introductionmentioning

confidence: 99%

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

Gu,

Wang

2024

Preprint

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“…At the same time, a refraction reverse learning strategy is designed to avoid falling into local optimality. Experimental data show that the ISSA algorithm makes the distribution of nodes more even and improves the coverage of WSN [7]. It can be found in the literature [5][6][7] that they all study swarm intelligence algorithms improve the performance of WSNs, from aspects such as node redundancy and coverage of homogeneous WSNs.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental data show that the ISSA algorithm makes the distribution of nodes more even and improves the coverage of WSN [7]. It can be found in the literature [5][6][7] that they all study swarm intelligence algorithms improve the performance of WSNs, from aspects such as node redundancy and coverage of homogeneous WSNs. Similar research includes the improved sticky algorithm proposed by Wei et al, simplified slime mold algorithm (SSMA) [8], the improved fruit fly optimization algorithm (change step of fruit fly optimization algorithm (CSFOA)) proposed by Song et al [9].…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

Ou,

Qin,

Zhou

et al. 2024

Symmetry

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For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability.

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“…The whale optimization algorithm (WOA) was used to solve portfolio optimization based on the FTSE100 index [3]. An improved sparrow search algorithm (ISSA) was developed to improve the high-intensity focused ultrasound (HIFU) technology that is used for tumor treatment [4]. A combination of tabu search (TS) and simulated annealing (SA) is used to solve the coupled task scheduling of the heterogeneous multiple automated guided vehicles (AGV) in a manufacturing system [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

Kusuma¹,

Dinimaharawati²

2023

IJACSA

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Adaptability becomes important in developing metaheuristic algorithms, especially in tackling stagnation. Unfortunately, almost all metaheuristics are not equipped with an adaptive approach that makes them change their strategy when stagnation happens during iteration. Based on this consideration, a new metaheuristic, called an adaptive balance optimizer (ABO), is proposed in this paper. ABO's unique strategy focuses on exploitation when improvement happens and switching to exploration during stagnation. ABO also uses a balanced strategy between exploration and exploitation by performing two sequential searches, whatever circumstance it faces. These sequential searches consist of one guided search and one random search. Moreover, ABO also deploys both a strict acceptance approach and a non-strict acceptance approach. In this work, ABO is challenged to solve a set of 23 classic functions as a theoretical optimization problem and a portfolio optimization problem as the use case for the practical optimization problem. In portfolio optimization, ABO should optimize the quantity of ten stocks in the energy and mining sector listed in the IDX30 index. In this evaluation, ABO is competed with five other metaheuristics: marine predator algorithm (MPA), golden search optimizer (GSO), slime mold algorithm (SMA), northern goshawk optimizer (NGO), and zebra optimization algorithm (ZOA). The simulation result shows that ABO is better than MPA, GSO, SMA, NGO, and ZOA in solving 21, 18, 16, 11, and 8, respectively, in solving 23 functions. Meanwhile, ABO becomes the third-best performer in solving the portfolio optimization problem.

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

Cited by 8 publications

References 31 publications

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

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