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Neural Computing and Applications
Published in: Neural Computing and Applications 12/2021

19-11-2020 | Original Article

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Author: Bryar A. Hassan

Published in: Neural Computing and Applications | Issue 12/2021

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Abstract

Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
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Literature
1.
Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst 159:20–50
2.
Jiang J, Meirong Xu, Meng X, Li K (2020) STSA: a sine tree-seed algorithm for complex continuous optimization problems. Phys A 537:122802
3.
Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, Rejeesh MR, Sundararaj R (2020) CCGPA-MPPT: cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovolt Res Appl 28:1128–1145
4.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
5.
Wang Y, Li H, Huang T, Li L (2014) Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl Soft Comput 18:232–247
6.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338MATH
7.
Bryar AH, Tarik AR (2020) Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation. Appl Math Comput 370:124919MathSciNetMATH
8.
Bryar AH, Tarik AR (2020) Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms. Data in Brief 28:105046
9.
Moradi M, Parsa S (2019) An evolutionary method for community detection using a novel local search strategy. Phys A 523:457–475
10.
Vinu S (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197
11.
Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710
12.
Dunia S, Ramzy A (2018) Chaotic sine-cosine optimization algorithms. Int J Soft Comput 13(3):108–122
13.
Yang X (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspir Comput 2(2):78–84
14.
Vinu S, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288
15.
Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126
16.
Marouani H, Fouad Y (2019) Particle swarm optimization performance for fitting of levy noise data. Phys A 514:708–714
17.
Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325
18.
Jiang J, Feng Y, Zhao J, Li K (2017) Data ABC: a fast ABC based energy-efficient live VM consolidation policy with data-intesive energy evaluation model. Future Gener Comput Syst 74:132–141
19.
Rashedi E, Nezamabadipour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248MATH
20.
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNet
21.
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Inf Sci 13:2592–2612
22.
Kashan AH (2014) League Championship Algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200
23.
Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H (2020) Barnacles Mating Optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng Appl Artif Intell 87:103330
24.
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
25.
Yang XS (2008) Nature-inspired metaheuristic algorithms, (Chapter 8). Luniver Press, Cambridge
26.
Guo M-W, Wang J-S, Yang X (2020) An chaotic firefly algorithm to solve quadratic assignment problem. Eng Lett 28(2):337–342
27.
Yang XS (2009) Firefly algorithms for multimodal optimization, Stochastic algorithms: foundations and applications. SAGA Lecture Notes Comput Sci 5792:169–178MATH
28.
Jagatheesan K, Anand B, Sen S, Samanta S (2020) Application of chaos-based firefly algorithm optimized controller for automatic generation control of two area interconnected power system with energy storage unit and UPFC. In: Applications of firefly algorithm and its variants, pp 173–191. Springer, Singapore
29.
Agarwal S, Singh AP, Anand N (2013) Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for nonlinear mathematical optimization functions. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), pp 1–8. IEEE
30.
Dash J, Dam B, Swain R (2020) Improved firefly algorithm based optimal design of special signal blocking IIR filters. Measurement 149:106986
31.
Dash S, Abraham A, Luhach AK, Mizera-Pietraszko J, Rodrigues JJPC (2020) Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis. Int J Distrib Sens Netw 16(1):1550147719895210
32.
Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetMATH
33.
Mirjalili SA (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133
34.
Guesmi T, Farah A, Marouani I, Alshammari B, Abdallah HH (2020) A new chaotic sine cosine algorithm for chance-constrained economic emission dispatch problem including wind energy. IET Renewable Power Generation
35.
Hui Lu, Wang X, Fei Z, Qiu M (2014) The effects of using chaotic map on improving the performance of multi-objective evolutionary algorithms. Math Probl Eng 2014:1–16
36.
Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317–325MathSciNetMATH
37.
Fu W, Wang K, Li C, Li X, Li Y, Zhong H (2018) Vibration trend measurement for a hydropower generator based on optimal variational mode decomposition and an LSSVM improved with chaotic sine cosine algorithm optimization. Meas Sci Technol 30(1):015012
38.
Liang X, Cai Z, Wang M, Zhao X, Chen H, Li C (2020) Chaotic oppositional sine–cosine method for solving global optimization problems. Eng Comput 36(3):1–17
39.
Tsai C, Huang K, Yang C, Chiang M (2015) A fast particle swarm optimization for clustering. Soft Comput 19(2):321–338
40.
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657
41.
Rizk-Allah RM, Hassanien AE, Bhattacharyya S (2018) Chaotic crow search algorithm for fractional optimization problems. Appl Soft Comput 71:1161–1175
42.
Jiang J, Yang Xi, Meng X, Li K (2020) Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems. Phys A 537:122621
43.
Coello CA (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127
44.
Onwubolu GC, Babu BV (2004) New optimization techniques in engineering. Springer, HeidelbergMATH
45.
Belegundu AD (1985) A study of mathematical programming methods for structural optimization. Int J Numer Methods Eng 21:1601–1623MathSciNetMATH
Metadata
Title
CSCF: a chaotic sine cosine firefly algorithm for practical application problems
Author
Bryar A. Hassan
Publication date
19-11-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05474-6

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