Hybrid ACO-CI Algorithm for Beam Design Problems
Authors: 
Ishaan R. Kale, Mandar S. Sapre, Ayush Khedkar, Kaustubh Dhamankar, Abhinav Anand, Aayushi SinghAuthors Info & Claims
Abstract
The complexity of real-world problems motivates the development of several optimization algorithms. In this paper, a novel hybrid metaheuristic algorithm is presented by combining the complementary properties of a swarm-based Ant Colony Optimization (ACO) and a socio-inspired Cohort Intelligence (CI) algorithm. It is referred to as hybrid ACO-CI. The ACO-CI algorithm is tested by solving unconstrained standard benchmark test functions. The proposed algorithm is further modified to solve constrained problems in the design engineering domain. The effectiveness of the proposed algorithm is evaluated relative to widely used metaheuristic algorithms. It is observed that the hybrid ACO-CI algorithm obtains comparable results with less computation cost.
References
[1]
Aladeemy M, Tutun S, and Khasawneh MT A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence Expert Syst Appl 2017 88 118-131
[2]
Basiri ME, Nemati S. A novel hybrid ACO-GA algorithm for text feature selection. In: 2009 IEEE Congress on Evolutionary Computation. 2009. p. 2561–68.
[3]
Cheng MY and Prayogo D Symbiotic organisms search: a new metaheuristic optimization algorithm Comput Struct 2014 139 98-112
[4]
Civicioglu P Backtracking search optimization algorithm for numerical optimization problems Appl Math Comput 2013 219 15 8121-8144
[5]
Dengiz B, Altiparmak F, and Belgin O Design of reliable communication networks: A hybrid ant colony optimization algorithm IIE Trans 2010 42 4 273-287
[6]
Dhavle SV, Kulkarni AJ, Shastri A, and Kale IR Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm Neural Comput Appl 2018 30 111-125
[7]
Dorigo M and Gambardella LM Ant colonies for the travelling salesman problem Biosystems 1997 43 2 73-81
[8]
Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995. p. 39–43.
[9]
Feng Y, Jia K, and He Y An improved hybrid encoding cuckoo search algorithm for 0–1 Knapsack problems Comput Intell Neurosci 2014 2014 1
[10]
Gandomi AH, Yang XS, and Alavi AH Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems Eng Comput 2013 29 17-35
[11]
Goldberg D Genetic algorithm in search. Optimization and machine learning 1989 Reading, MA Addison-Wesley
[12]
Huang KL and Liao CJ Ant colony optimization combined with taboo search for the job shop scheduling problem Comput Oper Res 2008 35 4 1030-1046
[13]
Iyer VH, Mahesh S, Malpani R, Sapre M, and Kulkarni AJ Adaptive range genetic algorithm: a hybrid optimization approach and its application in the design and economic optimization of shell-and-tube heat exchanger Eng Appl Artif Intell 2019 85 444-461
[14]
Jiang H, Zhang J, Xuan J, Ren Z, Hu Y. A hybrid ACO algorithm for the next release problem. In The 2nd International Conference on Software Engineering and Data Mining 2010. p. 166–71.
[15]
Jona JB and Nagaveni NN Ant-cuckoo colony optimization for feature selection in digital mammogram Pak J Biol Sci PJBS 2014 17 266-327
[16]
Kale IR and Kulkarni AJ Cohort intelligence algorithm for discrete and mixed variable engineering problems Int J Parallel Emergent Distrib Syst 2018 33 6 627-662
[17]
Kale IR, Kulkarni AJ, Satapathy SC. A socio-based cohort intelligence algorithm for engineering problems. In: Socio-cultural Inspired Metaheuristics. 2019. p.121–35.
[18]
Kale IR and Kulkarni AJ Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems Complex Intell Syst 2021 7 1565-1596
[19]
Kale IR and Khedkar A CI-SAPF for structural optimization considering buckling and natural frequency constraints Optimization methods for structural engineering 2023 Singapore Springer Nature Singapore 41-52
[20]
Kale IR, Pachpande MA, Naikwadi SP, and Narkhede MN Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm Int J Simul Multi Design Optim 2022 13 6
[21]
Kale IR, Khedkar A, and Sapre MS Truss structure optimization using constrained version of variations of cohort intelligence Optimization methods for structural engineering 2023 Singapore Springer Nature Singapore 67-78
[22]
Karaboga D. An idea based on honey bee swarm for numerical optimization, Vol. 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department; 2005. p. 1–10.
[23]
Karaboga D and Akay B A comparative study of artificial bee colony algorithm Appl Math Comput 2009 214 1 108-132
[24]
Kefayat M, Ara AL, and Niaki SN A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources Energy Convers Manage 2015 92 149-161
[25]
Krishnasamy G, Kulkarni AJ, and Paramesran R A hybrid approach for data clustering based on modified cohort intelligence and K-means Expert Syst Appl 2014 41 13 6009-6016
[26]
Kulkarni AJ and Shabir H Solving 0–1 knapsack problem using cohort intelligence algorithm Int J Mach Learn Cybern 2016 7 427-441
[27]
Kulkarni AJ, Baki MF, and Chaouch BA Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems Eur J Oper Res 2016 250 2 427-447
[28]
Kulkarni AJ, Durugkar IP, Kumar M. Cohort intelligence: a self supervised learning behavior. In 2013 IEEE international conference on systems, man, and cybernetics, 2013. p. 1396–400.
[29]
Luan J, Yao Z, Zhao F, and Song X A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization Math Comput Simul 2019 156 294-309
[30]
Mahi M, Baykan ÖK, and Kodaz H A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem Appl Soft Comput 2015 30 484-490
[31]
Menghour K and Souici-Meslati L Hybrid ACO-PSO based approaches for feature selection Int J Intell Eng Syst 2016 9 3 65-79
[32]
Mohan BC and Baskaran R A survey: ant colony optimization based recent research and implementation on several engineering domain Expert Syst Appl 2012 39 4 4618-4627
[33]
Nemati S, Basiri ME, Ghasem-Aghaee N, and Aghdam MH A novel ACO–GA hybrid algorithm for feature selection in protein function prediction Expert Syst Appl 2009 36 10 12086-12094
[34]
Patankar NS and Kulkarni AJ Variations of cohort intelligence Soft Comput 2018 22 6 1731-1747
[35]
Patel S, Kale IR, and Kulkarni AJ Hybridization of cohort intelligence and fuzzy logic (CIFL) for truss structure problems Optimization methods for structural engineering 2023 Singapore Springer Nature Singapore 79-93
[36]
Sapre MS, Kulkarni AJ, Shinde SS. Finite element mesh smoothing using cohort intelligence. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology: ICDECT 2017. Springer Singapore, 2019. p. 469–80
[37]
Sapre MS, Kulkarni AJ, Kale IR, Pande MS. Application of Cohort Intelligence Algorithm for Numerical Integration. In Intelligent Systems and Applications: Select Proceedings of ICISA 2022. Singapore: Springer Nature Singapore, 2023. p. 445–53
[38]
Sarmah DK and Kulkarni AJ Image steganography capacity improvement using cohort intelligence and modified multi-random start local search methods Arab J Sci Eng 2018 43 8 3927-3950
[39]
Sarmah DK, Kale IR. Cryptography algorithm based on cohort intelligence. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology: ICDECT 2017. Springer Singapore, 2019. p. 431–39
[40]
Shastri AS and Kulkarni AJ Multi-cohort intelligence algorithm: an intra-and inter-group learning behaviour based socio-inspired optimisation methodology Int J Parallel Emergent Distrib Syst 2018 33 6 675-715
[41]
Stützle T and Dorigo M ACO algorithms for the traveling salesman problem Evol Algorithms Eng Comput Sci 1999 4 163-183
[42]
Tam JH, Ong ZC, Ismail Z, Ang BC, and Khoo SY A new hybrid GA− ACO− PSO algorithm for solving various engineering design problems Int J Comput Math 2019 96 5 883-919
[43]
Tsai YL, Yang YJ, and Lin CH A dynamic decision approach for supplier selection using ant colony system Expert Syst Appl 2010 37 12 8313-8321
[44]
Yang XS Firefly algorithms for multimodal optimization International symposium on stochastic algorithms 2009 Springer
[45]
Yang XS Nature-inspired optimization algorithms: challenges and open problems J Comput Sci 2020 46 101104
[46]
Yucel M, Nigdeli SM Bekdaş G. Generation of an artificial neural network model for optimum design of I-beam with minimum vertical deflection. In 12th HSTAM international congress on mechanics. Thessaloniki, Greece. 2019.
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© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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.
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Publication History
Published: 22 February 2024
Accepted: 08 January 2024
Received: 26 March 2023
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