## 2 Related Works
[38] proposed a lightweight feature selection (FS) technique to improve classification for a real-world human activity recognition problem. The new FS approach, known as GBOGWO, intends to increase the performance of the gradient-based optimizer (GBO) algorithm by utilizing grey wolf optimizer's (GWO) operators. Support vector machine (SVM) is employed to categorize the activities once GBOGWO has selected suitable characteristics. Extensive tests were carried out to assess the efficiency of GBOGWO utilizing well-known UCI-HAR and WISDM datasets. The results suggested that GBOGWO enhanced the classification accuracy by an average of 98%. [PERSON] et al. [39] proposed a modified Harris hawks optimizer (HHO), providing an efficient method for addressing UPMSPs. The new approach, dubbed MHHO, employs the salp swarm algorithm (SSA) as a local search strategy to improve HHO's performance and reduce its computation time. Several tests were carried out to evaluate the characteristics of MHHO utilizing small and large problem scenarios. Furthermore, the proposed method was compared to other modern UPMSP methodologies. The MHHO outperformed the other methods in both small and large problem scenarios. In [PERSON] et al. [40], grey wolf optimizer (GWO), a swarm-based optimization method influenced by grey wolf behavior, was developed to address the power scheduling problem in a smart home (PSPSH). GWO features strong operations that are regulated by dynamic parameters that maintain exploration and exploitation actions in the search space. Simulations were performed using seven scenarios of power consumption and dynamic pricing schemes in order to assess the multi-objective PSPSH employing the SHB (BMO-PSPSH) approach. The performance of the BMO-PSPSH technique was compared to that of 17 other recent algorithms. [PERSON] et al. [41] developed a new salp swarm optimizer (SSA) version that, when paired with the sine cosine algorithm and the disruption operator, adjusts the location of followers in SSA using oscillating functions. This improvement aids in the discovery phase and avoids stagnation in a limited region. The disruption operator is used to increase variation in the population and maintain the balance between exploration and exploitation processes. The experimental outcomes were examined using 20 datasets, 4 of which had high complexity and a limited number of occurrences. [PERSON] et al. [42] proposed using a modified ant lion optimizer for effective simulation and positioning models to assist human operators with radiation heat transfer in an electric furnace, which is supported by the results of tests that suggest that the proposed algorithm may efficiently settle the system. [PERSON] et al. [43] provided an approach for customizing population-based meta-heuristics based on a heuristic model driven by simulated annealing. The method uses search operators from 10 meta-heuristic strategies as building blocks for new strategies. The method was tested on 107 benchmark high-dimensional functions under various experimental settings. The results show that it is feasible to create high-performing meta-heuristics with a variety of settings for each case study in an automated manner. [PERSON] et al. [44] presented a hyper-heuristic framework comprising numerous local optimization techniques and a pool of neighborhood structures. First, a two-stage hyper-heuristic framework is constructed for choosing a local search method and its operators. Second, an adaptive ranking system selects the best neighborhood constructs for the present local search method. It assesses the benefits of the local search in terms of quality and variety using entropy and adaptively updates the pool of potential neighborhood structures. Third, a population of solutions is employed to efficiently travel to various sections of the solution search space. [PERSON] et al. [45] proposed a hybrid AO and HHO technique merged with a nonlinear escape energy parameter and stochastic opposition-based learning technique.