Abstract
As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of international conference on neural networks, ICNN’95. IEEE, pp 1942–1948
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
Dorigo M, Caro GD (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation, CEC’99. 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 Glob Optim 39(3):459–471
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Yang XS, Suash D (2009) Cuckoo search via Lévy flights. In: Proceedings of 2009 world congress on nature & biologically inspired computing, NaBIC’09. IEEE, pp 210–214
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Design 43(3):303–315
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47
Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Heidaria AA, Mirjalilib S, Farisc H, Aljarahc I, Mafarjad M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comp Sy 97:849–872
Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53:2237–2264
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377
Qais MH, Hasanien HM, Alghuwainem S (2020) Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell 50:3926–3941
Hashim FA, Hussain K, Houssein EH, Mai MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51:1531–1551
Abualigah L, Yousri D, Elaziz MA, Ewees AA, Al-qaness MAA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250
Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021a) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408
Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021b) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36 (10):5887–5958
Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194
Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84–110
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Li S, Fang H, Liu X (2018) Parameter optimization of support vector regression based on sine cosine algorithm. Expert Syst Appl 91:63–77
Das S, Bhattacharya A, Chakraborty AK (2018) Solution of short-term hydrothermal scheduling using sine cosine algorithm. Soft Comput 22:6409–6427
Attia AF, Sehiemy RAE, Hasanien HM (2018) Optimal power flow solution in power systems using a novel sine-cosine algorithm. Int J Elec Power 99:331–343
Abdelsalam AA, Mansour HSE (2019) Optimal allocation and hourly scheduling of capacitor banks using sine cosine algorithm for maximizing technical and economic benefits. Electr Pow Compo Sys 47 (11-12):1025–1039
Bhookya J, Jatoth RK (2019) Optimal FOPID/PID controller parameters tuning for the AVR system based on sine-cosine-algorithm. Evol Intell 12:725–733
Dasgupta K, Roy PK, Mukherjee V (2020) Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm. Electr Pow Syst Res 178:106018
Dasgupta K, Roy PK, Mukherjee V (2022) Solution of short term integrated hydrothermal-solar-wind scheduling using sine cosine algorithm. Energy Strateg Rev 40:100824
Gabis AB, Meraihi Y, Mirjalili S, Ramdane-Cherif A (2021) A comprehensive survey of sine cosine algorithm: variants and applications. Artif Intell Rev 54:5469–5540
Elaziz MA, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500
Singh N, Singh SB (2017) A novel hybrid GWO-SCA approach for optimization problems. Eng Sci Technol 20(6):1586–1601
Nenavath H, Jatoth DK, Das DS (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43:1–30
Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043
Nenavath H, Jatoth RK (2019) Hybrid SCA-TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 31:5497–5526
Rizk-Allah RM (2018) Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems. J Comput Des Eng 5(2):249–273
Rizk-Allah RM (2019) An improved sine-cosine algorithm based on orthogonal parallel information for global optimization. Soft Comput 23:7135–7161
Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: a new hybrid PSO based on sine cosine algorithm and Levy flight for solving optimization problems. Appl Soft Comput 73:697–726
Issa M, Hassanien AE, Oliva D, Helmi A, Ziedan I, Alzohairy A (2018) ASCA-PSO: adaptive Sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst Appl 99:56–70
Guo W, Wang Y, Zhao F, Dai F (2019) Riesz fractional derivative elite-guided sine cosine algorithm. Appl Soft Comput 81:105481
Long W, Wu T, Liang X, Xu S (2019) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Syst Appl 123:108–126
Gupta S, Deep K (2019a) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230
Gupta S, Deep K (2019b) Improved sine cosine algorithm with crossover scheme for global optimization. Knowl Based Syst 165:374–406
Gupta S, Deep K (2020a) Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Appl 32:9521–9543
Gupta S, Deep K (2020b) A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Appl Intell 50:993–1026
Gupta S, Deep K, Engelbrecht AP (2020) A memory guided sine cosine algorithm for global optimization. Eng Appl Artif Intell 93:103718
Gupta S, Deep K, Mirjalili S, Kim JH (2020) A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization. Expert Syst Appl 154:113395
Feng Z, Liu S, Niu W, Li B, Wang W, Luo B, Miao S (2020) A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation. Knowl Based Syst 208:106461
Li N, Wang L (2020) Bare-bones based sine cosine algorithm for global optimization. J Comput Sci 47:101219
Guo W, Wang Y, Dai F, Xu P (2020) Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy. Eng Appl Artif Intell 94:103779
Li Y, Zhao Y, Liu J (2021) Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Appl Soft Comput 98:106933
Feng Z, Niu W, Liu S, Luo B, Miao S, Liu K (2020) Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies. J Hydrol 590:125223
Chen H, Wang M, Zhao X (2020) A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Appl Math Comput 369:124872
Fan Y, Wang P, Heidari AA, Wang M, Zhao X, Chen H, Li C (2020) Rationalized fruit fly optimization with sine cosine algorithm: a comprehensive analysis. Expert Syst Appl 157:113486
Khalilpourazari S, Pasandideh SHR (2020) Sine-cosine crow search algorithm: theory and applications. Neural Comput Appl 32:7725–7742
Raut U, Mishra S (2020) An improved sine-cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Appl Soft Comput 92:106293
Hassan BA (2021) CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Comput Appl 33:7011–7030
Hussain K, Neggaz N, Zhu W, Houssein EH (2021) An efficient hybrid sine-cosine harris hawks optimization for low and high-dimensional feature selection. Expert Syst Appl 176:114778
Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24
Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report, computational intelligence laboratory Zhengzhou University. Zhengzhou China, and Nanyang Technological University, Singapore
Awad NH, Ali MZ, Suganthan PN, Liang JJ, Qu BY (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical Report, Nanyang Technological University, Singapore and Jordan University of Science and Technology, Jordan, and Zhengzhou University, Zhengzhou China
Oustaloup A, Levron F, Mathieu B, Nanot FM (2000) Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Trans Circuits Syst I Fund Theory Appl 47(1):25–39
Gaidhane PJ, Nigam MJ (2018) A hybrid grey wolf optimizer and artificial bee colony algorithm forenhancing the performance of complex systems. J Comput Sci 27:284–302
Kumar A, Kumar V (2017) A novel interval type-2 fractional order fuzzy PID controller: design, performance evaluation, and its optimal time domain tuning. ISA Trans 68:251–275
Mudi RK, Pal NR (1999) A robust self-tuning scheme for PI- and PD-type fuzzy controllers. IEEE Trans Fuzzy Syst 7(1):2–16
Acknowledgements
This work was supported by the Natural Science Foundation of Gansu Province (Grant No. 21JR7RE181).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, Q., Ning, H. & Gong, J. An improved sine cosine algorithm with heterogeneous subpopulations for global optimization and fractional order PID controller design. Appl Intell 53, 18581–18604 (2023). https://doi.org/10.1007/s10489-023-04473-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-023-04473-z