###### Abstract
As a result, this particle's information is crucial for the population, and when it results in a less-than-ideal solution, it lowers the search efficiency of the algorithm. The absence of techniques for population mutation, the propensity to enter sub optimal and delayed convergence are some of the issues that restrict the practical uses of AEFA, despite the fact that it is an effective optimization technique. To address the shortcomings of AEFA, researchers have produced a number of variants in recent years. To improve the search efficiency and stability of AEFA, the artificial electric field algorithm with pattern search algorithm (AEFA-PS) was suggested for open switch network problem. It was shown to be capable of getting objective function fitness values of greater quality [30]. The improved artificial electric field algorithm (IAEFA) developed a unique method for calculating the electrostatic force that solved the limitations of AEFA such as early convergence and low search performance. The performance of IAEFA was assessed using 18 different test functions and it was discovered to be a more effective and efficient at solving the optimization problems [31]. Although AEFA has limited global search capabilities, it offers excellent local search capabilities. In order to overcome this, the adaptive search sine-cosine algorithm with artificial electric field algorithm (SC-AEFA) technique was created to dynamically balance the global and local search abilities of the algorithm. Experiments demonstrated the greater performance was achieved [15]. In order to overcome the problem of entering the sub-optimality in the original AEFA, the [PERSON] adopted artificial electric field algorithm (ML-AEFA) was proposed. In comparison to other algorithms, experiments revealed superior performance [32]. By instantly introducing the fittest particles into the population for the following generation and keeping the best particles from one iteration to the next, the elitism strategy boosts the potential of AEFA for optimization. Studies demonstrate the higher performance of AEFA [33]. The primary driving force for this study is to solve the aforementioned issues. Our goal is to successfully strike a balance between the capacity for exploration and exploitation and the enhancement of population variety. The suggested AEFA is based on Gaussian mutation specular reflection (GS) and local escaping operator (LEO) called GSLEO-AEFA. Firstly, the convergence efficiency of an algorithm is impacted by the low diversity and the use of starting agents that are randomly generated. The specular reflection learning approach is applied in this study to produce high-quality starting populations. Gaussian mutation is introduced for the mutation of the new population to further increase the variety of the starting population. Additionally, the local escape operator mechanism is added to the algorithm to update particle positions and prevent the population from settling into sub-optimal, thereby enhancing the capabilities for global search. It is also designed to advance the population toward the global optimum and achieve equilibrium between exploration and exploitation. During the forming of the GSLEO-AEFA, no modifications have been applied on the combined methods except the opposition-based learning (OBL). In this paper, we proposed an enhanced form of OBL called Gaussian mutation-based specular reflection (GS) and applied it to the standard AEFA algorithm. OBL is a technique that generates diverse candidate solutions in multimodal problems by having each solution learn from its opposite candidate [34]. It is a principle that inspired from the development of specular reflection. Specular reflection models the reflection of light in physics [35]. Our approach involves introducing the mutated form of specular reflection using Gaussian mutation only during the population initialization stage to enhance the diversity. This differs from the OBL introduced by [PERSON] et al.