### Literature Review  
Over the past few years, the significance of optimisation strategies has become considerably more apparent due to the rising challenges and complexities of DGP. To analyse the ambiguities in active distribution networks, previous research has employed a number of solution methodologies, including stochastic optimisation [6], Monte Carlo simulation [7], and probability statistical methods [8]. In addition, several methods of effective management, including reconfiguration of distribution networks, demand-side management, voltage profile improvement through reactive power compensation, and distributed energy storage technologies, have been implemented [9; 10]. Analytical algorithms are simple and quick to implement and execute. However, their inferences are usually indicative, as they are based on implicit assumptions and the model of the system. Nonlinear programming and sequential optimisation approaches are among the most effective deterministic methods available for DGP [11]. The important feature of the deterministic investigation approaches is that they ensure the identification of the global optimum; however, they are inadequate for large, distributed systems, which is also a drawback of the nonlinear programming method. On the other hand, heuristic approaches are resilient and offer relatively close form solutions for large, complicated DGP challenges without requiring any model of the system [12]. Meta-heuristic algorithms are artificial intelligence frameworks specifically for the purpose of solving complex combinatorial optimisation problems in the real world [13; 14]. To ensure optimum positioning and sizing for challenging objectives, which are usually problem-dependent and demand substantial versatility, researchers have adopted a wide variety of optimisation methodologies. Among them are the particle swarm optimisation (PSO), genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) algorithms, to mention a few. These strategies range from simple and basic approaches to comprehensive teaching and learning procedures. The objective of using GA in [15; 16] is to determine the optimum position and appropriate size of DG units so as to minimise power losses. In [17], the objective function is to reduce the economic and technical constraints of the distribution network for DG employment. To achieve better voltage stability in the distribution system, a stability index [18] for sizing DGs has been developed to include DGs at appropriate locations. The authors in [19] have considered that DGs be planned through GA in order to decrease the cost of energy loss and improve system advancement. The effectiveness of optimum positioning of multi-DGs considering power losses and reliability on distribution networks adopting the DE methodology is explored in [20]. To minimise system loss, PSO in [21] is used to calculate a cumulative performance index that includes power loss reduction, loss voltage profile enhancement, and voltage stability index. Due to the unpredictable nature of RESs, the artificial hummingbird algorithm in [22] has been carried out to plan the distribution grid. The reactive power management strategy and DG arrangement in [23] have been optimised to reduce cost-related characteristics. Flower-pollination-based optimisation is used to characterise the DG placement aspect [24], with the purposes being to minimise net operating costs, maximise voltage profile performance, improve voltage stability, and minimise real power loss. Furthermore, in [25], a systematic strategy for long-term management is proposed to enhance the voltage stability and reliability of the system. Despite these limitations, meta-heuristic approaches are being explored by a growing number of researchers who are attempting to find the optimal combination of meta-heuristics and other methodologies [26; 27]. Meta-heuristic techniques have also been employed to determine the optimum placement of EV charging stations by incorporating uncertainties in charging as well as in demand.