Abstract
The recently proposed Coati Optimization Algorithm (COA) is one of the swarm-based intelligence algorithms. In this study, the current COA algorithm is developed and Enhanced COA (ECOA) is proposed. There is an imbalance between the exploitation and exploration capabilities of the COA. To balance the exploration and exploitation capabilities of COA in the search space, the algorithm has been improved with two modifications. These modifications are those that preserve population diversity for a longer period of time during local and global searches. Thus, some of the drawbacks of COA in search strategies are eliminated. The achievements of COA and ECOA were tested in four different test groups. COA and ECOA were first compared on twenty-three classic CEC functions in three different dimensions (10, 20, and 30). Later, ECOA was tested on CEC-2017 with twenty-nine functions and on CEC-2020 with ten functions, and its success was demonstrated in different dimensions (5, 10, and 30). Finally, ECOA has been shown to be successful in different cycles (300, 500, and 1000) on Big Data Optimization Problems (BOP), which have high dimensions. Friedman and Wilcoxon tests were performed on the results, and the obtained results were analyzed in detail. According to the results, ECOA outperformed COA in all comparisons performed. In order to prove the success of ECOA, seven newly proposed algorithms (EMA, FHO, SHO, HBA, SMA, SOA, and JAYA) were selected from the literature in the last few years and compared with ECOA and COA. In the classical test functions, ECOA achieved the best results, surpassing all other algorithms when compared. It achieved the second-best results in CEC-2020 test functions and entered the top four in CEC-2017 and BOP test functions. According to the results, ECOA can be used as an alternative algorithm for solving small, medium, and large-scale continuous optimization problems.
Similar content being viewed by others
References
Zhao S, Zhang T, Ma S et al (2022) Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Appl Intell. https://doi.org/10.1007/s10489-022-03994-3
Saha C, Das S, Pal K, Mukherjee S (2014) A fuzzy rule-based penalty function approach for constrained evolutionary optimization. IEEE Trans Cybern 46(12):2953–2965
Hoos HH, Stützle T (2004) Stochastic local search: foundations and applications. Elsevier, Amsterdam
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Glover F (1989) “Tabu search”—part I. ORSA J Comput 1(3):190–206
Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73
Price KV (2013) Differential evolution. In: Zelinka I, Snášel V, Abraham A (eds) handbook of optimization. Springer, Berlin, pp 187–214
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: proceedings of ICNN'95-international conference on neural networks. vol 4. IEEE, pp 1942–1948
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
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
Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96(120–133):41
Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci 540:131–159
Sulaiman MH, Mustaffa Z, Saari MM et al (2022) Evolutionary mating algorithm. Neural Comput & Applic. https://doi.org/10.1007/s00521-022-07761-w
Dehghani M, Montazeri Z, Trojovská E, Trojovský P (2023) Coati Optimization Algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl-Based Syst 259:110011
Pandey HM (2016) Jaya a novel optimization algorithm: what, how and why? In: 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pp 728–730, doi: https://doi.org/10.1109/CONFLUENCE.2016.7508215.
Azizi M, Talatahari S, Gandomi AH (2022) Fire Hawk Optimizer: a novel metaheuristic algorithm. Artif Intell Rev. https://doi.org/10.1007/s10462-022-10173-w
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. https://doi.org/10.1016/j.matcom.2021.08.013. (ISSN 0378-4754)
Givi H, Marie H (2022) Skill optimization algorithm: a new human-based metaheuristic technique. Comput Mater Contin 74:179–202
Abd Elaziz M, Li L, Jayasena KPN, Xiong S (2020) Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution. Appl Math Model. https://doi.org/10.1016/j.apm.2019.10.069. (ISSN 0307-904X)
Goh SK, Tan KC, Al-Mamun A, Abbass HA (2015) Evolutionary big optimization (BigOpt) of signals. İn: 2015 IEEE Congress on Evolutionary Computation, CEC, IEEE, pp 3332–3339
Zhang Y, Zhou M, Jiang Z, Liu J (2015) A multi-agent genetic algorithm for big optimization problems. İn: 2015 IEEE Congress on Evolutionary Computation, CEC, IEEE, pp 703–707
Zhang Y, Liu J, Zhou M, Jiang Z (2016) A multi-objective memetic algorithm based on decomposition for big optimization problems. Memet Comput 8(1):45–61. https://doi.org/10.1007/s12293-015-0175-9
Elsayed S, Sarker R (2015) An adaptive configuration of differential evolution algorithms for big data. İn: IEEE Congress on Evolutionary Computation, CEC, IEEE, pp 695–702
Elsayed S, Sarker R (2016) Differential evolution framework for big data optimization. Memet Comput 8(1):17–33. https://doi.org/10.1007/s12293-015-0174-x
Cao Z, Wang L, Hei X, Jiang Q, Lu X, Wang X (2016). A phase based optimization algorithm for big optimization problems. İn: 2016 IEEE Congress on Evolutionary Computation, CEC, IEEE, 2016, pp 5209–5214
Aslan S, Karaboga D (2020) A genetic artificial bee Colony algorithm for signal reconstruction based big data optimization. Applied Soft Computing Journal 88:106053
Yildizdan G (2022) MJS: a modified artificial jellyfish search algorithm for continuous optimization problems. Neural Comput Appl 35(4):3483–3519
Chou JS, Truong DN (2021) A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl Math Comput 389:125535
Hakli H, Kiran MS (2020) An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization. Int J Mach Learn Cybern 11(9):2051–2076
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Storn R, Price K (1996) Minimizing the real functions of the ICEC'96 contest by differential evolution. İn: Proceedings of IEEE International Conference on Evolutionary Computation, 20–22 May 1996, pp 842–844, doi: https://doi.org/10.1109/ICEC.1996.542711
Tariq I et al (2020) MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Comput Appl 32:3101–3115. https://doi.org/10.1007/s00521-018-3808-3
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18
Ma J et al (2022) Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides 19(10):2489–2511. https://doi.org/10.1007/s10346-022-01923-6
Korkmaz S, Şahman MA, Cinar AC, Kaya E (2021) Boosting the oversampling methods based on differential evolution strategies for imbalanced learning. Appl Soft Comput 112:107787. https://doi.org/10.1016/j.asoc.2021.107787
Baş E (2022) Solving continuous optimization problems using the ımproved Jaya algorithm (IJaya). Artif Intell Rev 55:2575–2639. https://doi.org/10.1007/s10462-021-10077-1
Baş E (2022) Improved particle swarm optimization on based quantum behaved framework for big data optimization. Neural Process Lett. https://doi.org/10.1007/s11063-022-10850-5
Awad N, Ali M, Liang J, Qu B, Suganthan P (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou
Yue C, Price K, Suganthan P, Liang J, Ali M, Qu B, Awad N, Biswas P (2019) Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization. Comput. Intell. Lab., Zhengzhou Univ., Zhengzhou, China, Tech. Rep, 201911
Sahoo SK, Saha AK, Nama S et al (2022) An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy. Artif Intell Rev. https://doi.org/10.1007/s10462-022-10218-0
Sahoo SK, Saha AK (2022) A hybrid moth flame optimization algorithm for global optimization. J Bionic Eng 19:1522–1543. https://doi.org/10.1007/s42235-022-00207-y
Sahoo SK, Saha AK, Sharma S et al (2022) An enhanced moth flame optimization with mutualism scheme for function optimization. Soft Comput 26:2855–2882. https://doi.org/10.1007/s00500-021-06560-0
Nama S, Sharma S, Saha AK et al (2022) A quantum mutation-based backtracking search algorithm. Artif Intell Rev 55:3019–3073. https://doi.org/10.1007/s10462-021-10078-0
Sharma S, Chakraborty S, Saha AK et al (2022) mLBOA: a modified butterfly optimization algorithm with lagrange interpolation for global optimization. J Bionic Eng 19:1161–1176. https://doi.org/10.1007/s42235-022-00175-3
Liu R, Wang T, Zhou J, Hao X, Xu Y, Qiu J (2022) Improved African vulture optimization algorithm based on quasi-oppositional differential evolution operator. IEEE Access 10:95197–95218. https://doi.org/10.1109/ACCESS.2022.3203813
Abdollahzadeh B, Gharehchopogh FS, Khodadadi N, Mirjalili S (2022) Mountain Gazelle optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Adv Eng Softw 174:103282
Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36(10):5887–5958
Gharehchopogh FS (2022) An improved Harris Hawks optimization algorithm with multi-strategy for community detection in social network. J Bionic Eng 20(3):1175–1197
Gharehchopogh FS (2022) Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artif Intell Rev 56(6):5479–5543
Gharehchopogh FS (2022) An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems. J Bionic Eng 19(4):1177–1202
Gharehchopogh FS (2022) Advances in tree seed algorithm: a comprehensive survey. Arch Comput Methods Eng 30(1):427–455
Mohammadzadeh H, Gharehchopogh FS (2021) Feature selection with binary symbiotic organisms search algorithm for email spam detection. Int J Inf Technol Decis Mak 20(01):469–515
Naseri TS, Gharehchopogh FS (2022) A feature selection based on the Farmland fertility algorithm for improved intrusion detection systems. J Netw Syst Manage 30(3):1–27
Zaman HR, Gharehchopogh FS (2022) An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput 38(4):2797–2831
Mohammadzadeh H, Gharehchopogh FS (2021) A multi-agent system based for solving high-dimensional optimization problems: A case study on email spam detection. Int J Commun Syst 34(3):e4670
Gharehchopogh FS, Nadimi-Shahraki MH, Barshandeh S, Abdollahzadeh B, Zamani H (2022) Cqffa: a chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. J Bionic Eng 20(1):158–183
Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B (2022) Advances in sparrow search algorithm: a comprehensive survey. Arch Comput Methods Eng 2022:1–29
Gharehchopogh FS, Abdollahzadeh B, Arasteh B (2022) An improved farmland fertility algorithm with hyper-heuristic approach for solving travelling salesman problem
Shishavan ST, Gharehchopogh FS (2022) An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimed Tools Appl 84(18):25205–25231
Liang J, Ban X, Yu K, Qu B, Qiao K (2021) Differential evolution with rankings-based fitness function for constrained optimization problems. Appl Soft Comput 113:108016
Fallahi A, Mahnam M, Niaki STA (2022) A discrete differential evolution with local search particle swarm optimization to direct angle and aperture optimization in IMRT treatment planning problem. Appl Soft Comput 131:109798
Funding
This study was not funded by any institution.
Author information
Authors and Affiliations
Contributions
EB: Conceptualization, Investigation, Methodology, Software, Writing – review, Original draft & editing. GY: Conceptualization, Investigation, Methodology, Writing—review, Software, Original draft & editing.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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
Baş, E., Yildizdan, G. Enhanced Coati Optimization Algorithm for Big Data Optimization Problem. Neural Process Lett 55, 10131–10199 (2023). https://doi.org/10.1007/s11063-023-11321-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-023-11321-1