Enhancing the Water Lily Effect Algorithm Using a Fuzzy Inference System

Subject Areas : Information Technology in Engineering Design (ITED) Journal

Elham Dalirinia ¹ , Mehrdad Jalali ^{2 *} , Mahdi Yaghoobi ³ , Hamid Tabatabaee ⁴

1 - Mashhad Branch, Islamic Azad University
2 -
3 - Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
4 - Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran

Received: 2024-11-16 Accepted : 2024-12-24 Published : 2024-11-24

Keywords: Water Lily Effect Algorithm, Optimization, Fuzzy Inference System, Dragonfly Algorithm, Evolutionary Algorithm,

Abstract :

The Water Lily Effect Algorithm, introduced in 2024, is inspired by the processes of pollination and movement on water lily leaves. It utilizes concepts of static and dynamic swarm intelligence, modeled through dragonfly movements, to enhance the algorithm's exploratory capabilities. Additionally, local pollination and water movement on lily leaves contribute to its extraction efficiency. However, the algorithm lacks a precise mechanism for managing key parameters during exploration and extraction, with dragonfly movements being randomly defined in all scenarios. This limitation reduces both the accuracy and convergence speed of the optimization process.

This study proposes integrating a fuzzy inference system into the dragonfly movement mechanism to address these issues. By controlling the neighborhood radius, alignment, and cohesion movements, the algorithm achieves improved performance. When tested on 12 high-dimensional benchmark functions (50 dimensions), the fuzzy-enhanced Water Lily Algorithm showed a more than 49% improvement in convergence accuracy and over a 9% increase in convergence speed compared to the original algorithm.

References:

مراجع
[1] Y. J. Zheng, “Water wave optimization: A new nature-inspired metaheuristic,” Computers & Operations Research, vol. 55, pp. 1–11, 2015.
[2] S.W. Mahfoud, “A comparison of parallel and sequential niching method”, in: Proceedings of the Sixth International Conference on Genetic Algorithms,ICGA, pp. 136–143, 1995.
[3] Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah d,Majdi Mafarja, Huiling Chen,” Harris hawks optimization: Algorithm and applications”, Future Generation Computer Systems,2019.
[4] Dalirinia, E., Jalali, M., Yaghoobi, M. et al,” Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization” ,J Supercomput 80, pp.761–799 ,2024.
[5] Yassine Meraihi, Amar Ramdane-Cherif, Dalila Acheli & Mohammed Mahseur,” Dragonfly algorithm: a comprehensive review and applications” , Neural Computing and Applications.2020.
[6] Sree Ranjini,K.S.aS.Murugan, “Memory based Hybrid Dragonfly Algorithm for numerical optimization problems” Expert Systems with Applications, vol. 1, no.1, pp. 111-119, 2017.
[7] Singh,”Elite opposition learning and exponential function steps-based dragonfly algorithm for global optimization”, Expert Systems with Applications, vol. 2, no.1, pp. 51-60, 2017.
[8] Alaa Tharwat ،Thomas Gabel،Aboul Ella Hassanien ,” Pa rameter O ptimization of Supp ort Vector Machine Using Dragonfly Algorithm”, International Conference on Advanced Intelligent Systems and Informatics ,pp. 309-319,2017.
[9] Philip T. Daely , Soo Y. Shin,” Range based wireless node localization using Dragonfly Algorithm”, Eighth International Conference on Ubiquitous and Future Networks (ICUFN),pp. 55-61,2016.
[10] Gururaghav Raman،Gurupraanesh Raman،Chakkarapani Manickam،Saravana Ilango GanesanT,” Dragonfly Algorithm Based Global Maximum Power Point Tracker for Photovoltaic Systems”, International Conference in Swarm Intelligence,pp. 211-219,2016.
[11] C. Hema , Sharmila Sankar , Sandhya,” Energy efficient cluster based protocol to extend the RFID network lifetime using dragonfly algorithm”, International Conference on Communication and Signal Processing (ICCSP), pp.111-121,2016.
[12] Velamuri Suresh، Sreejith, “Generation dispatch of combined solar thermal systems using dragonfly algorithm” , Computing, pp.59–80,2016.
[13] MohammadJafari,Mohammad HosseinBayati Chaleshtari,” Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out”, European Journal of Mechanics - A/Solids,pp. 1-14,2017.
[14] Shounak Rushikesh Sugave , Suhas Haribhau Patil , B. Eswara Reddy,” DDF: Diversity Dragonfly Algorithm for cost-aware test suite minimization approach for software testing Sign In or Purchase”, International Conference on Intelligent Computing and Control Systems (ICICCS),pp. 34-45,2017.
[15] Majdi M. Mafarja ; Derar Eleyan ; Iyad Jaber ; Abdelaziz Hammouri ; Seyedali Mirjalili,” Binary Dragonfly Algorithm for Feature Selection”, International Conference on New Trends in Computing Sciences (ICTCS), pp.87-95,2017.
[16] Mohamed Abdel-Basset,Qifang Luo,Miao,Yongquan Zhou,” Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm”, International Conference on Intelligent Computing,pp. 491-502,2017.
[17] Shikha Agrawal,Jitendra Agrawal,” A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification”, Neural Computing and Applications, pp.60-67,2017.
[18] Bharat Singh Rana , Laxmi Srivastava,” Optimal location and sizing of STATCOM using Fuzzy-PSO approach”, IEEE 6th International Conference on Power Systems , pp.47-56,2017.
[19] YoucefSoufi,” Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system”, International Journal of Hydrogen Energy, pp.30-36,2017.
[20] Dey, Swati,” Rough-Fuzzy-GA-based design of Al alloys having superior cryogenic performance”, Materials and Manufacturing Processes, pp.87-95,2017.
[21] Anuja S. Desai , D. P. Gaikwad,” Real time hybrid intrusion detection system using signature matching algorithm and fuzzy-GA”, pp.100-106,2016.
[22] Kumar Abhishek , Saurav tta , Manoj Masanta , Siba Sankar Mahapatra,” Fuzzy embedded imperialist competitive algorithm (ICA) for multi-response optimization during machining of CFRP (Epoxy) composites”, International Conference on Advances in Mechanical, Industrial, Automation and Management Systems (AMIAMS), pp.33-39,2017.
[23] EhsanAmiri,” Efficient protocol for ta clustering by fuzzy Cuckoo Optimization Algorithm”, Applied Soft Computing, pp.93-101,2016.
[24] R.J.KuoaB.S.,WibowoabF.E.Zulvia,” Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time”, Applied Mathematical Modelling, pp.111-121,2016.
[25] JoydipDhara,SurbhiArora,” Designing fuzzy rule base using Spider Monkey Optimization Algorithm in cooperative framework”, Future Computing and Informatics Journal, pp.1-6,2017.
[26] ArashChaghari,” Fuzzy clustering based on Forest optimization algorithm”, Journal of King Saud University - Computer and Information Sciences, pp.43-49,2016.
[27] JiaZheng PeiYiXin SunHong Zhang,” Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm”, Science China Technological Sciences, pp.13-17,2017.
[28] Leticia Amador-AnguloOscar Castillo,” Comparative Study of Metrics That Affect in the Performance of the Bee Colony Optimization Algorithm Through Interval Type-2 Fuzzy Logic Systems”, North American Fuzzy Information Processing Society Annual Conference, pp.50-56,2017.
[29] M.Salehpour,A.Jamali,” A new aptive differential evolution optimization algorithm based on fuzzy inference system”, Engineering Science and Technology, an International Journal, pp.187-195,2017.
[30] Yan Huang, Wei Liu, Xiaogang Zang,” Improving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification”, Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE).2024.
[31] D. Jing, "Harris Harks Optimization Based Clustering With Fuzzy Routing for Lifetime Enhancing in Wireless Sensor Networks," in IEEE Access, vol. 12, pp. 12149-12163, 2024