Full Text:   <7125>

Summary:  <367>

Suppl. Mater.: 

CLC number: TP301

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2022-09-13

Cited: 0

Clicked: 2096

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chen CHEN

https://orcid.org/0000-0001-9354-6974

Kai MENG

https://orcid.org/0000-0002-6659-8530

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.12 P.1828-1847

http://doi.org/10.1631/FITEE.2200237


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


Author(s):  Kai MENG, Chen CHEN, Bin XIN

Affiliation(s):  School of Automation, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   3120195451@bit.edu.cn, xiaofan@bit.edu.cn, brucebin@bit.edu.cn

Key Words:  Swarm intelligence, Sparrow search algorithm, Adaptive parameter control strategy, Hybrid disturbance mechanism, Optimization problems


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.

@article{title="MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization",
author="Kai MENG, Chen CHEN, Bin XIN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="12",
pages="1828-1847",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200237"
}

%0 Journal Article
%T MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization
%A Kai MENG
%A Chen CHEN
%A Bin XIN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 12
%P 1828-1847
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200237

TY - JOUR
T1 - MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization
A1 - Kai MENG
A1 - Chen CHEN
A1 - Bin XIN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 12
SP - 1828
EP - 1847
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200237


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.

MSSSA:一种针对全局优化问题的多策略增强型麻雀搜索算法

孟凯1,2,陈晨1,2,辛斌1,2
1北京理工大学自动化学院,中国北京市,100081
2复杂系统智能控制与决策国家重点实验室,中国北京市,100081
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。

关键词:群智能;麻雀搜索算法;自适应参数控制策略;混合扰动机制;优化问题

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abdulhammed OY, 2022. Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. J Supercomput, 78(3):3266-3287.

[2]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.

[3]Askari Q, Saeed M, Younas I, 2020a. Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl, 161:113702.

[4]Askari Q, Younas I, Saeed M, 2020b. Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl-Based Syst, 195:105709.

[5]Aydilek İB, 2018. A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput, 66:232-249.

[6]Bäck T, Schwefel HP, 1993. An overview of evolutionary algorithms for parameter optimization. Evol Comput, 1(1):1-23.

[7]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.

[8]Chen HL, Yang CJ, Heidari AA, et al., 2020. An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Syst Appl, 154:113018.

[9]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.

[10]Deng J, Wang L, 2017. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol Comput, 32:121-131.

[11]Dhargupta S, Ghosh M, Mirjalili S, et al., 2020. Selective opposition based grey wolf optimization. Expert Syst Appl, 151:113389.

[12]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.

[13]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.

[14]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.

[15]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.

[16]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.

[17]Faramarzi A, Heidarinejad M, Stephens B, et al., 2020. Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst, 191:105190.

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

[19]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.

[20]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.

[21]Gupta S, Deep K, Mirjalili S, 2020. An efficient equilibrium optimizer with mutation strategy for numerical optimization. Appl Soft Comput, 96:106542.

[22]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.

[23]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.

[24]Heidari AA, Mirjalili S, Faris H, et al., 2019. Harris hawks optimization: algorithm and applications. Fut Gener Comput Syst, 97:849-872.

[25]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.

[26]Khishe M, Mosavi MR, 2020. Chimp optimization algorithm. Expert Syst Appl, 149:113338.

[27]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.

[28]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.

[29]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.

[30]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.

[31]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.

[32]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.

[33]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.

[34]Mittal H, Pal R, Kulhari A, et al., 2016. Chaotic Kbest gravitational search algorithm (CKGSA). Proc 9th Int Conf on Contemporary Computing, p.1-6.

[35]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.

[36]Nadimi-Shahraki MH, Taghian S, Mirjalili S, 2021. An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl, 166:113917.

[37]Naik MK, Panda R, Abraham A, 2021. Adaptive opposition slime mould algorithm. Soft Comput, 25(22):14297-14313.

[38]Nama S, Sharma S, Saha AK, et al., 2022. A quantum mutation-based backtracking search algorithm. Artif Intell Rev, 55(4):3019-3073.

[39]Poli R, Kennedy J, Blackwell T, 2007. Particle swarm optimization. Swarm Intell, 1(1):33-57.

[40]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.

[41]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.

[42]Rashedi E, Nezamabadi-Pour H, Saryazdi S, 2009. GSA: a gravitational search algorithm. Inform Sci, 179(13):2232-2248.

[43]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.

[44]Simon D, 2008. Biogeography-based optimization. IEEE Trans Evol Comput, 12(6):702-713.

[45]Srinivas M, Patnaik LM, 1994. Genetic algorithms: a survey. Computer, 27(6):17-26.

[46]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.

[47]Tian ZD, Chen H, 2021. A novel decomposition-ensemble prediction model for ultra-short-term wind speed. Energy Conv Manag, 248:114775.

[48]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.

[49]Tu JZ, Chen HL, Wang MJ, et al., 2021. The colony predation algorithm. J Bion Eng, 18(3):674-710.

[50]Wang MJ, Chen HL, 2020. Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput, 88:105946.

[51]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.

[52]Wolpert DH, Macready WG, 1997. No free lunch theorems for optimization. IEEE Trans Evol Comput, 1(1):67-82.

[53]Wu TQ, Yao M, Yang JH, 2016. Dolphin swarm algorithm. Front Inform Technol Electron Eng, 17(8):717-729.

[54]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.

[55]Xue JK, Shen B, 2020. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Contr Eng, 8(1):22-34.

[56]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.

[57]Yelghi A, Köse C, 2018. A modified firefly algorithm for global minimum optimization. Appl Soft Comput, 62:29-44.

[58]Zhang CL, Ding SF, 2021. A stochastic configuration network based on chaotic sparrow search algorithm. Knowl-Based Syst, 220:106924.

[59]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.

[60]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.

[61]Zhang XQ, Zhang YY, Ming ZF, 2021. Improved dynamic grey wolf optimizer. Front Inform Technol Electron Eng, 22(6):877-890.

[62]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.

[63]Zhu YL, Yousefi N, 2021. Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm. Int J Hydrogen Energy, 46(14):9541-9552.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE