Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

Abstract

The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem. In this paper, a comprehensive survey conducted about FDO and its applications. Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence. Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues. MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight. Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees. To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used. MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf Optimization (GWO), Chimp Optimization Algorithm (ChOA), Genetic Algorithm (GA), and Butterfly Optimization Algorithm (BOA). Statistical results proved that MFDO achieved significant performance compared to other algorithms. Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem. Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search Algorithm (GSA), GA, and Grasshopper Optimization Algorithm (GOA). .

A Multi-Objective Improved Hybrid Butterfly Artificial Gorilla Troop Optimizer for Node Localization in Wireless Sensor Groundwater Monitoring Networks

“…Figure 6 shows a comparison of various algorithms in terms of average error. At the beginning iterations (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), the ARO and HGS algorithms have higher average errors than the others, whereas the proposed iHBAGTO algorithm has the lowest average error. As the number of iterations increases, the average error decreases for all algorithms, but the iHBAGTO algorithm consistently outperforms the others with the lowest average error.…”

“…The algorithm combines the benefits of two popular algorithms, i.e., the Butterfly Algorithm (BA) and the gorilla troops optimizer (GTO), to achieve better results than individual algorithms. The proposed hybrid algorithm, iHBAGTO, combines the strengths of the BA and GTO algorithm using the search capabilities of the BA to find the optimal anchor node positions and the exploration capabilities of the GTO to refine the positions of the anchor nodes [20]. Combining the Butterfly Optimization Algorithm (BOA) with the artificial gorilla troop optimizer (GTO) brings several benefits to enhancing the localization accuracy in wireless sensor networks (WSNs).…”

Review of Feature Selection Methods Using Optimization Algorithm

“…Ref. PSO (Zahran and Kanaan, 2009;Liu et al, 2011;Sahu and Mishra, 2012;Xue, Zhang and Browne, 2012;Aghdam and Heidari, 2015;Ahmad, 2015;Kumar Gupta et al, 2015;Brezočnik, 2017;Abualigah, Khader and Hanandeh, 2018;Qasim and Algamal, 2018) WOA (Zheng et al, 2018;Bui et al, 2019;Nematzadeh et al, 2019;Mandal et al, 2021;Too, Mafarja and Mirjalili, 2021) GA (Emary et al, 2015;Li et al, 2017;Nirmala Sreedharan et al, 2018;Too et al, 2018;Johari and Gupta, 2021;Kitonyi and Segera, 2021) DF Al-Jumaily, 2008, 2011;Al-Ani, Alsukker and Khushaba, 2013;Bhattacharyya et al, 2014;Zorarpac\i and Özel, 2016;Vivekanandan and Iyengar, 2017;Dixit, Mani and Bansal, 2020;Hancer, 2020;Zhang et al, 2020;Hancer, Xue and Zhang, 2022) FDO (Abdulkhaleq et al, no date;Guha et al, 2020;Chiu et al, 2021;Abbas et al, 2022;Salih, Mohammed and Abdul, 2022) ABC (Hancer et al, 2018;Arslan and Ozturk, 2019;Zhang et al, 2019;Wang et al, 2020;Almarzouki, 2022) Ant colony (Al-Ani, 2005;Kanan, Faez and...…”