Abstract
The Chimp Optimization Algorithm (ChOA) is a cutting-edge swarm intelligence algorithm that models the social status ties and hunting behavior of chimps to solve complex optimization problems. Although ChOA is known for its simplicity and efficiency, it may encounter challenges such as convergence speed and local optima. This study presents a comprehensive and competitive multi-strategy-enhanced chimp optimization algorithm (SLDChOA), which comprehensively enhances the optimization performance of the algorithm through four strategies. Firstly, a low-difference Sobol sequence strategy is used to initialize the chimp population to increase the diversity of the initial population. Secondly, different location update strategies are adopted according to different iteration stages. The early iteration stage employs the Lévy flight-based location update strategy to help chimps explore the space more abundantly and improve the global search ability of the algorithm. In contrast, the proposed probability-based elitist operation strategy is used in the late iteration stage to help the chimps obtain higher-quality optimal solutions and improve the convergence accuracy and speed of the algorithm. Finally, the dimension learning-based hunting search strategy is introduced to facilitate information sharing among chimps and enable the algorithm to jump out of the local optimum effectively. To demonstrate its comprehensive performance, SLDChOA is compared with 17 state-of-the-art algorithms on 23 traditional benchmark functions, CEC 2014 and CEC 2019 test sets (totaling 63 test functions). Moreover, its efficacy and excellence are further demonstrated in 4 well-known engineering optimization issues and 2 feature selection problems of multimodal Parkinson’s speech datasets. A series of simulations demonstrate that SLDChOA has good comprehensive merit-seeking ability and is extremely competitive.
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References
Trakhtenbrot BA (1984) A survey of Russian approaches to perebor (brute-force searches) algorithms. Ann Hist Comput 6(4):384–400
Chinnasamy S, Ramachandran M, Amudha M, Ramu K (2022) A review on hill climbing optimization methodology. Recent Trends Manag Commer 3(1):1
Pop PC, Cosma O, Sabo C, Sitar CP (2023) A comprehensive survey on the generalized traveling salesman problem. Eur J Oper Res
Porumbel DC (2012) Heuristic algorithms and learning techniques: applications to the graph coloring problem. 4OR 10(4):393–394
Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137:106040
Holl J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4. IEEE, pp 1942–1948
Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE, pp 1470–1477
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39:459–471
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl-Based Syst 251:109215
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mohammed H, Rashid T (2023) Fox: a fox-inspired optimization algorithm. Appl Intell 53(1):1030–1050
Naruei I, Keynia F (2021) A new optimization method based on coot bird natural life model. Expert Syst Appl 183:115352
Seyyedabbasi A, Kiani F (2022) Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Eng Comput 1:1–25
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84
Rao RV, Savsani VJ, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Hertz A, Taillard E, de Werra D (1997) Tabu search. Local search in combinatorial optimization, pp 121–136
Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549
Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338
Tian A-Q, Chu S-C, Pan J-S, Cui H, Zheng W-M (2020) A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability 12(3):767
Pan J-S, Tian A-Q, Snášel V, Kong L, Chu S-C (2022) Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with taguchi method. Energy 251:123863
Pan J-S, Tian A-Q, Chu S-C, Li J-B (2021) Improved binary pigeon-inspired optimization and its application for feature selection. Appl Intell 51(12):8661–8679
Zhao Y, Huang C, Zhang M, Lv C (2023) Colma: a Chaos-based mayfly algorithm with opposition-based learning and levy flight for numerical optimization and engineering design. J Supercomput 1:1–47
Zeng L, Li Y, Zhang H, Li M, Wang S (2023) A mixed Harris Hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems. J Supercomput 1:1–54
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Sörensen K (2015) Metaheuristics-the metaphor exposed. Int Trans Oper Res 22(1):3–18
Hu T, Khishe M, Mohammadi M, Parvizi G-R, Karim SHT, Rashid TA (2021) Real-time covid-19 diagnosis from x-ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm. Biomed Signal Process Control 68:102764
Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731
Zayed ME, Zhao J, Li W, Elsheikh AH, Abd Elaziz M, Yousri D, Zhong S, Mingxi Z (2021) Predicting the performance of solar dish stirling power plant using a hybrid random vector functional link/chimp optimization model. Sol Energy 222:1–17
Jia H, Sun K, Zhang W, Leng X (2021) An enhanced chimp optimization algorithm for continuous optimization domains. Complex Intell Syst 1:1–18
Kaidi W, Khishe M, Mohammadi M (2022) Dynamic levy flight chimp optimization. Knowl-Based Syst 235:107625
Gong S-P, Khishe M, Mohammadi M (2022) Niching chimp optimization for constraint multimodal engineering optimization problems. Expert Syst Appl 198:116887
Kaur M, Kaur R, Singh N, Dhiman G (2021) Schoa: a newly fusion of sine and cosine with chimp optimization algorithm for hls of datapaths in digital filters and engineering applications. Eng Comput 1:1–29
Dhiman G (2021) SSC: a hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications. Knowl-Based Syst 222:106926
Uzer MS, Inan O (2023) A novel feature selection using binary hybrid improved whale optimization algorithm. J Supercomput 1:1–26
Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34
Luo W, Jin H, Li H, Fang X, Zhou R (2020) Optimal performance and application for firework algorithm using a novel chaotic approach. IEEE Access 8:120798–120817
Joe S, Kuo FY (2003) Remark on algorithm 659: implementing Sobol’s quasirandom sequence generator. ACM Trans Math Softw (TOMS) 29(1):49–57
Lee C-Y, Yao X (2004) Evolutionary programming using mutations based on the lévy probability distribution. IEEE Trans Evol Comput 8(1):1–13
Gomes C.P, Selman B, Crato N (1997) Heavy-tailed distributions in combinatorial search. In: International Conference on Principles and Practice of Constraint Programming. Springer, pp 121–135
Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917
Solis FJ, Wets RJ-B (1981) Minimization by random search techniques. Math Oper Res 6(1):19–30
Pelusi D, Mascella R, Tallini L, Nayak J, Naik B, Abraham A (2018) Neural network and fuzzy system for the tuning of gravitational search algorithm parameters. Expert Syst Appl 102:234–244
Ahmed AM, Rashid TA, Saeed SAM (2021) Dynamic cat swarm optimization algorithm for backboard wiring problem. Neural Comput Appl 33(20):13981–13997
Rather SA, Bala PS (2021) Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding. Expert Syst 38(7):12717
Ma C, Huang H, Fan Q, Wei J, Du Y, Gao W (2022) Grey wolf optimizer based on aquila exploration method. Expert Syst Appl 205:117629
Naik MK, Panda R, Abraham A (2021) Adaptive opposition slime mould algorithm. Soft Comput 25(22):14297–14313
Khishe M, Nezhadshahbodaghi M, Mosavi MR, Martín D (2021) A weighted chimp optimization algorithm. IEEE Access 9:158508–158539
Khishe M (2022) Greedy opposition-based learning for chimp optimization algorithm. Artif Intell Rev 1:1–31
Zhang Q, Du S, Zhang Y, Wu H, Duan K, Lin Y (2022) A novel chimp optimization algorithm with refraction learning and its engineering applications. Algorithms 15(6):189
Tanabe R, Fukunaga A.S (2014) Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658–1665
Hadi AA, Mohamed AW, Jambi KM (2019) LSHADE-SPA memetic framework for solving large-scale optimization problems. Complex Intell Syst 5:25–40
Wu R, Huang H, Wei J, Ma C, Zhu Y, Chen Y, Fan Q (2023) An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement. Expert Syst Appl 215:119421
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190
Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018
Bernardino HS, Barbosa HJ, Lemonge AC (2007) A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 646–653
Kumari CL, Kamboj VK, Bath S, Tripathi SL, Khatri M, Sehgal S (2023) A boosted chimp optimizer for numerical and engineering design optimization challenges. Eng Comput 39(4):2463–2514
Gandomi AH, Yun GJ, Yang X-S, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simul 18(2):327–340
Frank A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
Khanesar MA, Teshnehlab M, Shoorehdeli MA (2007) A novel binary particle swarm optimization. In: 2007 Mediterranean Conference on Control & Automation. IEEE, pp 1–6
Al-Tashi Q, Kadir SJA, Rais HM, Mirjalili S, Alhussian H (2019) Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7:39496–39508
Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31:171–188
Funding
This work was in part supported by the Key Research and Development Project of Hubei Province (No. 2020BAB114 & 2023BAB094), the Key Project of Science and Technology Research Program of Hubei Educational Committee (No. D20211402), the Project of Xiangyang Industrial Institute of Hubei University of Technology (No. XYYJ2022C04), and the Open Foundation of Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System (No. HBSEES201903 & HBSEES202106).
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QY, SW, MH and LZ conceived the experiments, QY conducted the experiments. All authors reviewed the manuscript.
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Yuan, Q., Wang, S., Hu, M. et al. SLDChOA: a comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design. J Supercomput 80, 3589–3643 (2024). https://doi.org/10.1007/s11227-023-05617-1
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DOI: https://doi.org/10.1007/s11227-023-05617-1