###### Abstract  
During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to combine the strengths of the different methods. In this study, one of the recently developed metaheuristic algorithms, artificial electric field algorithm (AEFA), has been used, to improve its converge speed and the ability to avoid the local optimum points of the given problems. To address these issues, Gaussian mutation specular reflection learning (GS) and local escaping operator (LEO) have been added to the essential steps on AEFA and called GSLEO-AEFA. In order to observe the effect of the applied features, 23 benchmark functions as well as engineering and real-world application problems were tested and compared with the other algorithms. [PERSON] and [PERSON] rank-sum statistical tests, and complexity analyses were also conducted to measure the performance of GSLEO-AEFA. The overall effectiveness of the algorithm among the compared algorithms obtained in between 84.62-92.31%. According to the achieved results, it can be seen that GSLEO-AEFA has precise optimization accuracy even in changing dimensions, especially in engineering optimization problems. artificial electric field algorithm; local escaping operator; Gaussian mutation; specular reflection learning +
Footnote †: journal: Medical  
These principles include the principle of gravity, light, and refraction [11]. They are being implemented in several areas to solve engineering challenges [12; 13; 14; 15]. Evolution theory mimicking algorithms through various combination and permutations of populations obtain the best solution. The main benefit of this method is that the individuals with the best solutions combine to create the next generation [16; 17; 18; 19]. The exploitation and exploration patterns of behavior are basically the patterns the metaheuristic algorithms will follow as it advances towards the optimal solution. The discovery of new search space areas is called exploration. Exploitation is the procedure of locating and investigating the most promising areas in the hunt for potentially superior solutions [20]. Any metaheuristic algorithms share a potential problem in trying to maintain an adequate balance between these two patterns since they contradict each other [21]. In 2019, [PERSON] and [PERSON] suggested the artificial electric field algorithm (AEFA) [22]. The leadership influence of the lead particle, in other words, the particle with the strongest charge and the behavior of the other particles as they are drawn along the search area by the lead particle, serve as the foundation for the search mechanism of the AEFA. This approach ensures that the algorithm is able to exploit promising areas for the best solution consistently [15]. AEFA has earned a reputation and has been effectively applied to several sectors in recent years due its various benefits, including ease of implementation, low computing cost, and great search efficiency. A few of the areas in which AEFA has proved a useful optimization algorithm are tumor detection [23], multi-objective problem [24], parameter extraction for photovoltaic model [25], assembly line balancing optimization problem [26], and feature selection [27]. Despite AEFA being applicable in many areas, prior research has revealed that it has significant shortcomings such as premature convergence, exploration and exploitation imbalance, and a propensity to become stuck at sub-optima [15; 28; 29], which are also present in other metaheuristic algorithms. However, the attraction force mechanism of the AEFA has a significant impact on population diversification. In the typical AEFA, a dominating particle directs the search process. This causes the population to gather around a single particle.