Abstract
This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of “premature convergence,” which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The MATLAB code for DCSO algorithm can be found in the below repository link: https://github.com/aramahmed/DCSO-Algorithm/.
References
Baskan O (ed) (2016) Optimization algorithms: methods and applications. BoD–Books on Demand.
Xin-She Y (2010) An introduction with metaheuristic applications. Engineering Optimization. John Wiley, New York
Voß S (2000) Meta-heuristics: the state of the art. In: Workshop on local search for planning and scheduling. Springer, Berlin, pp 1–23
Yang XS (2020) Nature-inspired optimization algorithms: challenges and open problems. J ComputSci 46:101104
Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press
Pappula L, Ghosh D (2018) Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl Soft Comput 66:473–491
Nie X, Wang W, Nie H (2017) Chaos quantum-behaved cat swarm optimization algorithm and its application in the PV MPPT. Comput Intell Neurosci
Orouskhani M., Mansouri M, Teshnehlab M (2011) Average-inertia weighted cat swarm optimization. In: International conference in swarm intelligence. Springer, Berlin, pp 321–328
Ahmed AM, Rashid TA, Saeed SAM (2020) Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci 2020
Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. In: Computational intelligence for multimedia big data on the cloud with engineering applications. Academic Press, London, pp 185–231
Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert SystAppl 149:113338
Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J InnovComputInf Control 3(1):163–173
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Steinberg L (1961) The backboard wiring problem: a placement algorithm. SIAM Rev 3(1):37–50
Burkard RE, Karisch SE, Rendl F (1997) QAPLIB—a quadratic assignment problem library. J Glob Optim 10(4), 391–403. Accessed 27 June 2020
Tosun U (2015) On the performance of parallel hybrid algorithms for the solution of the quadratic assignment problem. Eng Appl Artif Intell 39:267–278
Fard AMF, Hajiaghaei-Keshteli M (2018) A bi-objective partial interdiction problem considering different defensive systems with capacity expansion of facilities under imminent attacks. Appl Soft Comput 68:343–359
Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence. Springer, Berlin, pp 854–858
Price KV, Awad NH, Ali MZ, Suganthan PN (2018) The 100-digit challenge: problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Nanyang Technological University, Singapore
Nguyen TT, Vo DN, Dinh BH (2018) An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems. Energy 155:930–956
Cheng S, Shi Y, Qin Q, Zhang Q, Bai R (2014) Population diversity maintenance in brain storm optimization algorithm. J ArtifIntell Soft Comput Res 4(2):83–97
Hussain K, Salleh MNM, Cheng S, Shi Y (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31(11):7665–7683
Morales-Castañeda B, Zaldivar D, Cuevas E, Fausto F, Rodríguez A (2020) A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput 54:100671
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
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.
Supplementary Information
Below is the link to the electronic supplementary material.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Ahmed, A.M., Rashid, T.A. & Saeed, S.A.M. Dynamic Cat Swarm Optimization algorithm for backboard wiring problem. Neural Comput & Applic 33, 13981–13997 (2021). https://doi.org/10.1007/s00521-021-06041-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06041-3