###### Abstract
Improved HHO is presented in [34] to conduct global optimization and select the optimal threshold values for multi-level picture segmentation challenges. Furthermore, HHOSSA, an enhanced HHO that considers the SSA, is suggested, and this work has produced a multi-level picture segmentation technique. The Grasshopper Optimization Algorithm (GOA), a novel swarm intelligence approach, was inspired by grasshopper foraging and swarming behavior in the outdoors [26]. The GOA approach has demonstrated its worth in the literature by efficiently addressing a wide range of optimization issues in a variety of domains [13]. The Grey Wolf Optimization (GWO) optimization approach replicates grey wolf hunting methods in the wild [25]. GWO is based on group hunting methods used by grey wolves as part of their social systems. The ABC algorithm was developed by modeling the foraging behavior of honey bees [20], and it has been utilized to solve many different engineering problems so far [35; 36]. Combining HHO and the African Culture Optimization Algorithm (AVOA), a hybrid HHO-AVOA optimization method is introduced in [37]. It addresses path-planning challenges for differential wheeled mobile robots (DWMRs) in static and dynamic environments, considering kinematic constraints. The HHO with Memory-Saving Strategy (HHO-MSS) optimization method is introduced [38]. This new method for optimizing the parameters of the Power System Stabilizer and Virtual Inertia in a renewable microgrid power system with diverse energy sources is suggested. The RSRFT approach was developed by integrating the Reptile Search Algorithm (RSA)with the Red Fox Algorithm (RFO) and the Triangular Mutation Operator (TMO) [39]. This method was optimized to enhance the exploration and exploitation phases. An enhanced dung beetle optimization algorithm (EDBO) has been introduced to address nonlinear optimization challenges with multiple constraints in manufacturing [40]. The algorithm's ability to explore and exploit is strengthened by refining its rolling, dancing, and foraging processes. To address the low convergence accuracy and the tendency to fall into the local optima of the traditional Harris Hawks Optimization (HHO) algorithm, a Compound Improved HHO (CIHHO) is proposed [41]. Enhancements include the dynamic adjustment of energy, the Versoria function for improved local exploration, Levy flight for escaping local optima, and random white noise for increased accuracy. A hybrid optimization algorithm (HSSOGA) that combines the exploitation capability of Sperm Swarm Optimization (SSO) with the exploration capability of the Gravitational Search Algorithm (GSA) is proposed [42]. This study demonstrates that the hybrid method outperforms the standard SSO and GSA in terms of faster convergence and better ability to escape local extremes across most benchmark functions. The Whale Optimization Algorithm (WOA) is an algorithm that simulates humpback whale hunting stages [28]. The WOA is based on the fact that humpback whales encircle and catch their prey in a smaller area due to the air bubbles they create. The Moth-Flame Optimization (MFO) algorithm has been proposed, inspired by a moth's navigation method in nature [43]. Five algorithms consisting of different combinations of the WOA, PSO, and Levy flight are proposed in [5]. The best of these five algorithms, the WOALFVWPSO algorithm, was compared to the literature for 23 benchmark and pressure vessel design problems, and it was found that this algorithm produced more competitive solutions for most problems. A novel wrapper-based feature selection approach, referred to as BWPLFS, is introduced, combining binary WOA, PSO, and Levy flight in [11]. The method is evaluated using 10 benchmark datasets and compared against various algorithms from the literature. The results indicate that the proposed method effectively identifies the most relevant features.