“…Yet, these techniques face several obstacles, such as the collapse of local minima, death traps at local minima, the necessary for numerous iterations, and the dependence on initial requirements when determining the optimal parameters. For this reason, researchers have developed metaheuristic optimization techniques, such as the PSO (Jagatheesan et al, 2015), moth swarm algorithm (MSO) (Magdy et al, 2018), electric optimization algorithm (EOA) (Dahab et al, 2020), improved lightning attachment algorithm (ILAO) (Khamies et al, 2021), moth flame optimization (MFO) (Nandi et al, 2019), lightning attachment optimizer (LAO) (Mohamed et al, 2020), Fitness Dependent Optimizer (FDO) (Daraz et al, 2020a), path finder algorithm (PFA) (Priyadarshani et al 2020), genetic algorithm (GA) (Soleimani and Mazloum, 2018), hybrid teaching learningbased algorithm with pattern search (hTLBO-PS) (Dillip Khamari et al, 2020), hybridized sine cosine algorithm with FDO (hSCA-FDO) (Daraz et al, 2022), water cycle algorithm (WCA) (Kumari and Shankar, 2018) and hybridized DE with PS (Pradhan et al, 2021). Elkasem et al in (Elkasem et al, 2022) used the cascaded configuration of tilt derivative with tilt integral term controller to adjust their gains using improved form of chaos game algorithm.…”