Abstract
Heap-Based Optimizer (HBO) is one of the recently proposed metaheuristic algorithms inspired by the corporate rank hierarchy. In this study, opposition and Cauchy-based search mechanisms are integrated into HBO which is applied for the design of high-order Butterworth active filters, for the first time, comparing with six outstanding metaheuristics introduced for the last year (2020). In addition, the proposed algorithm has been adapted to solve the benchmark presented in the IEEE-CEC 2020 competition to demonstrate its effectiveness against numerical functions. The analog filter design is a challenging problem due to its discrete topology and complex search space. This study provides a comprehensive review for the design of these filters with the up-to-date algorithms: firstly, the performance of each metaheuristic is analyzed by its error value performance, the required number of iterations to achieve this error value; secondly, a statistical test is conducted to validate its performances; as the third, convergence abilities of algorithms are compared with respect to the total design process error values versus an iteration number graph. The passive component values of the Butterworth active filter are selected within the E24 and E96 industrial series so as to make the realization of designs easier to the real world. To demonstrate the suitability of the components obtained by the algorithms to the real world, the amplitude response of the associated design is given. The simulation results demonstrate the effectiveness of the proposed algorithm over the contestant algorithms, and its capability of solving these case design problems. This extensive work can also provide guidelines to the researchers for future studies.




















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References
Thede L (2004) Practical analog and digital filter design, 1st edn. Artech House Publishers, UK
Winder S (2002) Analog and digital filter design, 2nd edn. Newnes, USA
Horrocks DH, Spittle MC (1993) Component value selection for active filters using genetic algorithms. In: Proceedings of IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pp 131–136
Vural RA, Yildirim T (2010) Component value selection for analog active filter using particle swarm optimization. In: The 2nd International conference on computer and automation engineering, ICCAE, pp 25–28. https://doi.org/10.1109/ICCAE.2010.5452009
Vural RA, Yildirim T, Kadioglu T, Basargan A (2012) Performance evaluation of evolutionary algorithms for optimal filter design. IEEE Trans Evol Comput 16:135–147. https://doi.org/10.1109/TEVC.2011.2112664
Kalinli A (2014) Optimal circuit design using immune algorithm. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-540-30220-9_4
Kalinli A (2006) Component value selection for active filters using parallel tabu search algorithm. Int J Electron Commun 60:85–92. https://doi.org/10.1016/j.aeue.2005.03.001
Min J, Zhenkun Y, Zhaohui G (2007) Optimal components selection for analog active filters using clonal selection algorithms. Lect Notes Comput Sci 4681:950–959. https://doi.org/10.1007/978-3-540-74171-8_96
Vural RA, Bozkurt U, Yildirim T (2013) Analog active filter component selection with nature inspired metaheuristics. Int J Electron Commun 67:197–205. https://doi.org/10.1016/j.aeue.2012.07.009
Dogan B, Olmez T (2015) Vortex search algorithm for the analog active filter component selection problem. Int J Electron Commun 69(9):1243–1253. https://doi.org/10.1016/j.aeue.2015.05.005
El BA, Bachir B, Izeddine Z (2020) Analog active filter component selection using genetic algorithm. Embed Syst Artif Intell. https://doi.org/10.1007/978-981-15-0947-6_16
Sattar D, Salim R (2020) A smart metaheuristic algorithm for solving engineering problems. Eng Comput. https://doi.org/10.1007/s00366-020-00951-x
De BP, Kar R, Mandal D, Ghoshal SP (2015) Optimal selection of components value for analog active filter design using simplex particle swarm optimization. Int J Mach Learn Cyber 6:621–636. https://doi.org/10.1007/s13042-014-0299-0
De BP, Kar R, Mandal D, Ghoshal SP (2015) Optimal analog active filter design using craziness-based particle swarm optimization algorithm. Int J Numer Model 28:593–609. https://doi.org/10.1002/jnm.2040
De BP, Kar R, Mandal D, Ghoshal SP (2015) Particle swarm optimization with aging leader and challengers for optimal design of analog active filters. Circuits Syst Signal Process 34:707–737. https://doi.org/10.1007/s00034-014-9872-8
Durmuş B (2018) Optimal components selection for active filter design with average differential evolution algorithm. Int J Electron Commun 94:293–302. https://doi.org/10.1016/j.aeue.2018.07.021
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. https://doi.org/10.1007/s00366-020-01233-2
Talatahari S, Azizi M (2020) Chaos game optimization: a novel metaheuristic algorithm. Artif Intell Rev. https://doi.org/10.1007/s10462-020-09867-w
Konstantinos Z, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559. https://doi.org/10.1016/j.cie.2020.106559
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377
Weiguo Z, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300. https://doi.org/10.1016/j.engappai.2019.103300
Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055
Askari Q, Mehreen S, Irfan Y (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl 161:113702. https://doi.org/10.1016/j.eswa.2020.113702
Dib N, El-Asir B (2018) Optimal design of analog active filters using symbiotic organisms search. Int J Numer Model Electron Netw Dev Fields 31:e2323. https://doi.org/10.1002/jnm.2323
Mancini R (2003) Op amps for everyone, 1st edn. Newnes, UK
Shahryar R, Hamid RT, Magdy MAS (2008) Opposition versus randomness in soft computing techniques. Appl Soft Comput 8(2):906–918. https://doi.org/10.1016/j.asoc.2007.07.010
Lin J (2015) Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems. Nonlinear Dyn 80(1):209–219. https://doi.org/10.1007/s11071-014-1861-8
Gupta S, Deep K, Heidari AA, Moayedi H, Chen H (2019) Harmonized salp chain-built optimization. Eng Comput. https://doi.org/10.1007/s00366-019-00871-5
Kuyu YÇ, Vatansever F (2021) Modified forensic-based investigation algorithm for global optimization. Eng Comput. https://doi.org/10.1007/s00366-021-01322-w
Yang X, Huang Z (2011) Artificial bee colony with dynamic Cauchy mutation for numerical optimization. J Inf Comput Sci. 8(15):3371–3376
Paiva FAP, Silva CRM, Leite IVO, Marcone MHF, Costa JAF (2017) Modified bat algorithm with Cauchy mutation and elite opposition-based learning. In: 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI, pp 1–6. https://doi.org/10.1109/la-cci.2017.8285715.
Yue CT, Price KV, Suganthan PN, Liang JJ, Ali MZ, Qu BY, Awad NH, Partha PB (2019) Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization. Technical Report, 201911.
Wilcoxon F (1992) Individual comparisons by ranking methods, breakthroughs in statistics, vol 2. Springer, USA
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Kuyu, Y.Ç., Vatansever, F. Heap-based optimizer embedded with search strategies applied to high-order analog filter designs: a comparative study with up-to-date metaheuristics. Neural Comput & Applic 35, 1447–1467 (2023). https://doi.org/10.1007/s00521-022-07835-9
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DOI: https://doi.org/10.1007/s00521-022-07835-9