MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG; Chen CHEN; Bin XIN

doi:10.1631/FITEE.2200237

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MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Regular Papers | Updated：2022-12-16

- MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization
- MSSSA：一种针对全局优化问题的多策略增强型麻雀搜索算法
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 12, Pages: 1828-1847(2022)
- Affiliations：
  
  1.School of Automation, Beijing Institute of Technology, Beijing 100081, China
  2.State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing 100081, China
- Author bio：
  
  E-mail: 3120195451@bit.edu.cn;
  ‡Corresponding author
  brucebin@bit.edu.cn
- Funds：
- DOI：10.1631/FITEE.2200237
  CLC： TP301
- Received：30 May 2022，
  
  Accepted：13 September 2022，
  
  Published：0 December 2022
- Accepted：
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Kai MENG, Chen CHEN, Bin XIN. MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1828-1847.
DOI：

Kai MENG, Chen CHEN, Bin XIN. MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1828-1847. DOI： 10.1631/FITEE.2200237.

摘要

麻雀搜索算法(SSA)是一种新的元启发式优化方法，具有简单和灵活的优点。然而，在处理多模态优化问题时，该算法仍存在早熟收敛、探索与开发不平衡等缺陷。针对上述问题，本文提出一种多策略增强的麻雀搜索算法(MSSSA)。首先，引入混沌映射以获取高质量的初始种群，并采用对立学习策略增加种群的多样性。其次，设计了一种自适应参数控制策略，以在全局探索与局部开发之间保持适当的平衡。最后，在个体更新阶段嵌入混合扰动机制，以避免算法陷入局部最优。为了验证所提方法的有效性，在IEEE CEC2014和IEEE CEC2019测试集的40个函数，以及10个不同维度的经典函数上进行了大量的实验。实验结果表明，与一些先进的算法相比，所提出的MSSSA表现出突出的优化性能。该算法还成功地应用于两个工程优化问题，证明了MSSSA在解决实际问题方面的优越性。

Abstract

The sparrow search algorithm (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However

SSA still faces challenges of premature convergence and imbalance between exploration and exploitation

especially when tackling multimodal optimization problems. Aiming to deal with the above problems

we propose an enhanced variant of SSA called the multi-strategy enhanced sparrow search algorithm (MSSSA) in this paper. First

a chaotic map is introduced to obtain a high-quality initial population for SSA

and the opposition-based learning strategy is employed to increase the population diversity. Then

an adaptive parameter control strategy is designed to accommodate an adequate balance between exploration and exploitation. Finally

a hybrid disturbance mechanism is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA

a large number of experiments are implemented

including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering optimization problems. The results demonstrate the superiority of the MSSSA in addressing practical problems.

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Abdulhammed OY , 2022 . Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm . J Supercomput , 78 ( 3 ): 3266 - 3287 . doi: 10.1007/s11227-021-03989-w http://doi.org/10.1007/s11227-021-03989-w

Ahmadianfar I , Heidari AA , Gandomi AH , et al. , 2021 . RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method . Expert Syst Appl , 181 : 115079 . doi: 10.1016/j.eswa.2021.115079 http://doi.org/10.1016/j.eswa.2021.115079

Askari Q , Saeed M , Younas I , 2020a . Heap-based optimizer inspired by corporate rank hierarchy for global optimization . Expert Syst Appl , 161 : 113702 . doi: 10.1016/j.eswa.2020.113702 http://doi.org/10.1016/j.eswa.2020.113702

Askari Q , Younas I , Saeed M , 2020b . Political optimizer: a novel socio-inspired meta-heuristic for global optimization . Knowl-Based Syst , 195 : 105709 . doi: 10.1016/j.knosys.2020.105709 http://doi.org/10.1016/j.knosys.2020.105709

Aydilek İB , 2018 . A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems . Appl Soft Comput , 66 : 232 - 249 . doi: 10.1016/j.asoc.2018.02.025 http://doi.org/10.1016/j.asoc.2018.02.025

Bäck T , Schwefel HP , 1993 . An overview of evolutionary algorithms for parameter optimization . Evol Comput , 1 ( 1 ): 1 - 23 . doi: 10.1162/evco.1993.1.1.1 http://doi.org/10.1162/evco.1993.1.1.1

Chang ZZ , Gu QH , Lu CW , et al. , 2022 . 5G private network deployment optimization based on RWSSA in open-pit mine . IEEE Trans Ind Inform , 18 ( 8 ): 5466 - 5476 . doi: 10.1109/TII.2021.3132041 http://doi.org/10.1109/TII.2021.3132041

Chen HL , Yang CJ , Heidari AA , et al. , 2020 . An efficient double adaptive random spare reinforced whale optimization algorithm . Expert Syst Appl , 154 : 113018 . doi: 10.1016/j.eswa.2019.113018 http://doi.org/10.1016/j.eswa.2019.113018

Chen WN , Zhang J , Lin Y , et al. , 2013 . Particle swarm optimization with an aging leader and challengers . IEEE Trans Evol Comput , 17 ( 2 ): 241 - 258 . doi: 10.1109/TEVC.2011.2173577 http://doi.org/10.1109/TEVC.2011.2173577

Deng J , Wang L , 2017 . A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem . Swarm Evol Comput , 32 : 121 - 131 . doi: 10.1016/j.swevo.2016.06.002 http://doi.org/10.1016/j.swevo.2016.06.002

Dhargupta S , Ghosh M , Mirjalili S , et al. , 2020 . Selective opposition based grey wolf optimization . Expert Syst Appl , 151 : 113389 . doi: 10.1016/j.eswa.2020.113389 http://doi.org/10.1016/j.eswa.2020.113389

Dhivyaprabha TT , Subashini P , Krishnaveni M , 2018 . Synergistic fibroblast optimization: a novel nature-inspired computing algorithm . Front Inform Technol Electron Eng , 19 ( 7 ): 815 - 833 . doi: 10.1631/FITEE.1601553 http://doi.org/10.1631/FITEE.1601553

Ding SX , Chen C , Xin B , et al. , 2018 . A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches . Appl Soft Comput , 63 : 249 - 267 . doi: 10.1016/j.asoc.2017.09.012 http://doi.org/10.1016/j.asoc.2017.09.012

Eskandar H , Sadollah A , Bahreininejad A , et al. , 2012 . Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems . Comput Struct , 110-111 : 151 - 166 . doi: 10.1016/j.compstruc.2012.07.010 http://doi.org/10.1016/j.compstruc.2012.07.010

Fan Q , Chen ZJ , Li Z , et al. , 2021 . A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems . Eng Comput , 37 ( 3 ): 1851 - 1878 . doi: 10.1007/s00366-019-00917-8 http://doi.org/10.1007/s00366-019-00917-8

Fang QC , Shen B , Xue JK , 2022 . A new elite opposite sparrow search algorithm-based optimized LightGBM approach for fault diagnosis . J Amb Intell Human Comput , early access . doi: 10.1007/s12652-022-03703-5 http://doi.org/10.1007/s12652-022-03703-5

Faramarzi A , Heidarinejad M , Stephens B , et al. , 2020 . Equilibrium optimizer: a novel optimization algorithm . Knowl-Based Syst , 191 : 105190 . doi: 10.1016/j.knosys.2019.105190 http://doi.org/10.1016/j.knosys.2019.105190

Jr Fister I , Yang XS , Fister I , et al. , 2013 . A brief review of nature-inspired algorithms for optimization . https://arxiv.org/abs/1307.4186 https://arxiv.org/abs/1307.4186

Gai JB , Zhong KY , Du XJ , et al. , 2021 . Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm . Measurement , 185 : 110079 . doi: 10.1016/j.measurement.2021.110079 http://doi.org/10.1016/j.measurement.2021.110079

Gao GQ , Xin B , 2019 . A-STC: auction-based spanning tree coverage algorithm formotion planning of cooperative robots . Front Inform Technol Electron Eng , 20 ( 1 ): 18 - 31 . doi: 10.1631/FITEE.1800551 http://doi.org/10.1631/FITEE.1800551

Gupta S , Deep K , Mirjalili S , 2020 . An efficient equilibrium optimizer with mutation strategy for numerical optimization . Appl Soft Comput , 96 : 106542 . doi: 10.1016/j.asoc.2020.106542 http://doi.org/10.1016/j.asoc.2020.106542

Hashim FA , Houssein EH , Mabrouk MS , et al. , 2019 . Henry gas solubility optimization: a novel physics-based algorithm . Fut Gener Comput Syst , 101 : 646 - 667 . doi: 10.1016/j.future.2019.07.015 http://doi.org/10.1016/j.future.2019.07.015

Hashim FA , Hussain K , Houssein EH , et al. , 2021 . Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems . Appl Intell , 51 ( 3 ): 1531 - 1551 . doi: 10.1007/s10489-020-01893-z http://doi.org/10.1007/s10489-020-01893-z

Heidari AA , Mirjalili S , Faris H , et al. , 2019 . Harris hawks optimization: algorithm and applications . Fut Gener Comput Syst , 97 : 849 - 872 . doi: 10.1016/j.future.2019.02.028 http://doi.org/10.1016/j.future.2019.02.028

Heidari AA , Aljarah I , Faris H , et al. , 2020 . An enhanced associative learning-based exploratory whale optimizer for global optimization . Neur Comput Appl , 32 ( 9 ): 5185 - 5211 . doi: 10.1007/s00521-019-04015-0 http://doi.org/10.1007/s00521-019-04015-0

Khishe M , Mosavi MR , 2020 . Chimp optimization algorithm . Expert Syst Appl , 149 : 113338 . doi: 10.1016/j.eswa.2020.113338 http://doi.org/10.1016/j.eswa.2020.113338

Li CH , Song Y , Wang FY , et al. , 2017 . A chaotic coverage path planner for the mobile robot based on the Chebyshev map for special missions . Front Inform Technol Electron Eng , 18 ( 9 ): 1305 - 1319 . doi: 10.1631/FITEE.1601253 http://doi.org/10.1631/FITEE.1601253

Li SM , Chen HL , Wang MJ , et al. , 2020 . Slime mould algorithm: a new method for stochastic optimization . Fut Gener Comput Syst , 111 : 300 - 323 . doi: 10.1016/j.future.2020.03.055 http://doi.org/10.1016/j.future.2020.03.055

Li XJ , Gu JN , Sun XH , et al. , 2022 . Parameter identification of robot manipulators with unknown payloads using an improved chaotic sparrow search algorithm . Appl Intell , 52 ( 9 ): 10341 - 10351 . doi: 10.1007/s10489-021-02972-5 http://doi.org/10.1007/s10489-021-02972-5

Liang JJ , Qin AK , Suganthan PN , et al. , 2006 . Comprehensive learning particle swarm optimizer for global optimization of multimodal functions . IEEE Trans Evol Comput , 10 ( 3 ): 281 - 295 . doi: 10.1109/TEVC.2005.857610 http://doi.org/10.1109/TEVC.2005.857610

Liu JC , Wei JH , Heidari AA , et al. , 2022 . Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis . Comput Biol Med , 144 : 105356 . doi: 10.1016/j.compbiomed.2022.105356 http://doi.org/10.1016/j.compbiomed.2022.105356

Liu JN , Peng H , Wu ZJ , et al. , 2020 . Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment . Appl Intell , 50 ( 4 ): 1289 - 1315 . doi: 10.1007/s10489-019-01600-7 http://doi.org/10.1007/s10489-019-01600-7

Long W , Jiao JJ , Liang XM , et al. , 2022 . A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine . Artif Intell Rev , early access . doi: 10.1007/s10462-022-10233-1 http://doi.org/10.1007/s10462-022-10233-1

Mittal H , Pal R , Kulhari A , et al. , 2016 . Chaotic Kbest gravitational search algorithm (CKGSA) . Proc 9 th Int Conf on Contemporary Computing , p. 1 - 6 . doi: 10.1109/IC3.2016.7880252 http://doi.org/10.1109/IC3.2016.7880252

Moosavi SHS , Bardsiri VK , 2019 . Poor and rich optimization algorithm: a new human-based and multi populations algorithm . Eng Appl Artif Intell , 86 : 165 - 181 . doi: 10.1016/j.engappai.2019.08.025 http://doi.org/10.1016/j.engappai.2019.08.025

Nadimi-Shahraki MH , Taghian S , Mirjalili S , 2021 . An improved grey wolf optimizer for solving engineering problems . Expert Syst Appl , 166 : 113917 . doi: 10.1016/j.eswa.2020.113917 http://doi.org/10.1016/j.eswa.2020.113917

Naik MK , Panda R , Abraham A , 2021 . Adaptive opposition slime mould algorithm . Soft Comput , 25 ( 22 ): 14297 - 14313 . doi: 10.1007/s00500-021-06140-2 http://doi.org/10.1007/s00500-021-06140-2

Nama S , Sharma S , Saha AK , et al. , 2022 . A quantum mutation-based backtracking search algorithm . Artif Intell Rev , 55 ( 4 ): 3019 - 3073 . doi: 10.1007/s10462-021-10078-0 http://doi.org/10.1007/s10462-021-10078-0

Poli R , Kennedy J , Blackwell T , 2007 . Particle swarm optimization . Swarm Intell , 1 ( 1 ): 33 - 57 . doi: 10.1007/s11721-007-0002-0 http://doi.org/10.1007/s11721-007-0002-0

Qin AK , Huang VL , Suganthan PN , 2009 . Differential evolution algorithm with strategy adaptation for global numerical optimization . IEEE Trans Evol Comput , 13 ( 2 ): 398 - 417 . doi: 10.1109/TEVC.2008.927706 http://doi.org/10.1109/TEVC.2008.927706

Rao RV , Savsani VJ , Vakharia DP , 2011 . Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems . Comput-Aided Des , 43 ( 3 ): 303 - 315 . doi: 10.1016/j.cad.2010.12.015 http://doi.org/10.1016/j.cad.2010.12.015

Rashedi E , Nezamabadi-Pour H , Saryazdi S , 2009 . GSA: a gravitational search algorithm . Inform Sci , 179 ( 13 ): 2232 - 2248 . doi: 10.1016/j.ins.2009.03.004 http://doi.org/10.1016/j.ins.2009.03.004

Ruan WY , Duan HB , 2020 . Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization . Front Inform Technol Electron Eng , 21 ( 5 ): 740 - 748 . doi: 10.1631/FITEE.2000066 http://doi.org/10.1631/FITEE.2000066

Simon D , 2008 . Biogeography-based optimization . IEEE Trans Evol Comput , 12 ( 6 ): 702 - 713 . doi: 10.1109/TEVC.2008.919004 http://doi.org/10.1109/TEVC.2008.919004

Srinivas M , Patnaik LM , 1994 . Genetic algorithms: a survey . Computer , 27 ( 6 ): 17 - 26 . doi: 10.1109/2.294849 http://doi.org/10.1109/2.294849

Storn R , Price K , 1997 . Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces . J Glob Optim , 11 ( 4 ): 341 - 359 . doi: 10.1023/A:1008202821328 http://doi.org/10.1023/A:1008202821328

Tian ZD , Chen H , 2021 . A novel decomposition-ensemble prediction model for ultra-short-term wind speed . Energy Conv Manag , 248 : 114775 . doi: 10.1016/j.enconman.2021.114775 http://doi.org/10.1016/j.enconman.2021.114775

Tizhoosh HR , 2005 . Opposition-based learning: a new scheme for machine intelligence . Proc Int Conf on Computational Intelligence for Modelling, Control and Automation and Int Conf on Intelligent Agents, Web Technologies and Internet Commerce , p. 695 - 701 . doi: 10.1109/CIMCA.2005.1631345 http://doi.org/10.1109/CIMCA.2005.1631345

Tu JZ , Chen HL , Wang MJ , et al. , 2021 . The colony predation algorithm . J Bion Eng , 18 ( 3 ): 674 - 710 . doi: 10.1007/s42235-021-0050-y http://doi.org/10.1007/s42235-021-0050-y

Wang MJ , Chen HL , 2020 . Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis . Appl Soft Comput , 88 : 105946 . doi: 10.1016/j.asoc.2019.105946 http://doi.org/10.1016/j.asoc.2019.105946

Wang X , Liu J , Hou T , et al. , 2021 . The SSA-BP-based potential threat prediction for aerial target considering commander emotion . Def Technol , 18 ( 11 ): 2097 - 2106 . doi: 10.1016/j.dt.2021.05.017 http://doi.org/10.1016/j.dt.2021.05.017

Wolpert DH , Macready WG , 1997 . No free lunch theorems for optimization . IEEE Trans Evol Comput , 1 ( 1 ): 67 - 82 . doi: 10.1109/4235.585893 http://doi.org/10.1109/4235.585893

Wu TQ , Yao M , Yang JH , 2016 . Dolphin swarm algorithm . Front Inform Technol Electron Eng , 17 ( 8 ): 717 - 729 . doi: 10.1631/FITEE.1500287 http://doi.org/10.1631/FITEE.1500287

Xin B , Chen J , Peng ZH , et al. , 2010 . An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization . Sci China Inform Sci , 53 ( 5 ): 980 - 989 . doi: 10.1007/s11432-010-0114-9 http://doi.org/10.1007/s11432-010-0114-9

Xue JK , Shen B , 2020 . A novel swarm intelligence optimization approach: sparrow search algorithm . Syst Sci Contr Eng , 8 ( 1 ): 22 - 34 . doi: 10.1080/21642583.2019.1708830 http://doi.org/10.1080/21642583.2019.1708830

Yang YT , Chen HL , Heidari AA , et al. , 2021 . Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts . Expert Syst Appl , 177 : 114864 . doi: 10.1016/j.eswa.2021.114864 http://doi.org/10.1016/j.eswa.2021.114864

Yelghi A , Köse C , 2018 . A modified firefly algorithm for global minimum optimization . Appl Soft Comput , 62 : 29 - 44 . doi: 10.1016/j.asoc.2017.10.032 http://doi.org/10.1016/j.asoc.2017.10.032

Zhang CL , Ding SF , 2021 . A stochastic configuration network based on chaotic sparrow search algorithm . Knowl-Based Syst , 220 : 106924 . doi: 10.1016/j.knosys.2021.106924 http://doi.org/10.1016/j.knosys.2021.106924

Zhang GH , Wang L , Xing KY , 2021 . Dual-space co-evolutionary memetic algorithm for scheduling hybrid differentiation flowshop with limited buffer constraints . IEEE Trans Syst Man Cybern Syst , 52 ( 11 ): 6822 - 6836 . doi: 10.1109/TSMC.2021.3102658 http://doi.org/10.1109/TSMC.2021.3102658

Zhang XM , Wang DD , Fu ZH , et al. , 2020 . Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation . Appl Math Model , 86 : 74 - 91 . doi: 10.1016/j.apm.2020.05.016 http://doi.org/10.1016/j.apm.2020.05.016

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Zhang Z , He R , Yang K , 2022 . A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm . Adv Manuf , 10 ( 1 ): 114 - 130 . doi: 10.1007/s40436-021-00366-x http://doi.org/10.1007/s40436-021-00366-x

Zhu YL , Yousefi N , 2021 . Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm . Int J Hydrogen Energy , 46 ( 14 ): 9541 - 9552 . doi: 10.1016/j.ijhydene.2020.12.107 http://doi.org/10.1016/j.ijhydene.2020.12.107

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