## 1 Introduction  
The integration of renewable energy source-based distributed generation (RES-DG), such as wind turbine generators (WTGs), in power systems is continuously increasing due to extensive technical developments, as well as clean and low-cost energy production [1; 2]. The wind power share of worldwide electricity at the end of 2018 was 4.8%, which could increase to 19% after ten years, thereby avoiding more than three billion tons of CO\({}_{2}\) a year [3,4]. Aside from the benefits, RES-DGs change the radial distribution network (DN) into a meshed network and cause bidirectional power flow, which changes the fault current level (FCL) [5], resulting in conventional protection systems facing new challenges, most notably the overcurrent relay coordination (ORC) problem [6, 7, 8, 9]. Overcurrent relay (OCR) measures the FCL and sends a trip signal after a typical operating time. The faulty portion is isolated from the healthy system if proper coordination is sustained between the primary and backup OCRs [10]. This relay coordination is maintained by delaying the upstream relays with a suitable time called the coordination time interval (CTI). Optimal relay settings, such as the pickup current (Ip) and time multiplier setting (TMS), play a vital role in achieving optimum ORC. These relay settings are fixed at predefined FCL and connected load [11, 12]. The integration of wind farms (WF) into distribution systems is intermittent, depending on the operating conditions of the WTG, which mainly depend on the stochastic behavior of wind speed and direction. This results in changes in the FCL with WD, which affect the relay setting, thereby causing miscoordination problems [13, 14]. Thus, the relay settings should be adaptively updated in line with the operating conditions of the WTG. To determine optimal relay settings, two approaches are used: conventional approaches (CA) [15, 16, 17, 18, 19, 20, 21] and optimization approaches (OA) [22, 23, 24, 25, 26, 27, 28, 29, 30]. Conventional approaches involve the predetermination and analysis of fault currents during abnormal conditions and system contingencies [15], which are dependent on the network configuration; they include curve-fitting techniques [16] computer programming software [17], graphical selection [18], minimum breakpoint set [19], and linear graph theory [20]. In a complex meshed network with multiple RES-DG, conventional methods are not suitable, as the time to update the relay settings during contingencies is prolonged and ORC becomes impracticable [21]. Thus, authors strived to propose optimization approaches. The overall purpose of optimization approaches in the ORC problem is to obtain the minimum possible operation time by optimally adjusting the OR settings subjected to constraints. Firstly, the ORC problem is formulated as linear programming (LP), in which one variable from Ip and TMS is predetermined and the second is optimized [22, 23]. In complex networks, the results of LP may be trapped in local minima. Hence, the ORC problem is formulated as nonlinear programming (NLP), where both settings of the relay are optimized at the same time, which is often formulated with metaheuristic techniques such as particle swarm optimization (PSO) [25], seeker optimization algorithm (SOA) [26], genetic algorithm (GA) [27], evolutionary optimization algorithm (EOA) [28], teaching learning-based algorithm (TLBO) [29], ant colony optimization (ACO), gravitational search algorithm (GSA) [30], symbiotic organism search optimization technique (SOSO) [31], and extended continuous domain ant colony optimization [32]. Some hybrid optimization approaches (HOAs) were also proposed in the literature by combining the benefits of different OAs. It is seen that HOAs produce good results in the form of less computational time, better accuracy, and reliability toward global optimization [33, 34].