Pareto multi-objective study of sizing and siting electric vehicle charging stations and distributed generation using an enhanced hunger game search optimizer

This study introduces the Enhanced Hunger Games Search (EHGS) algorithm for jointly determining the optimal sites and sizes of electric vehicle charging stations (EVCS) and distributed generation (DG) units within radial distribution networks. Building on the original Hunger Games Search, EHGS keeps its full hunger-driven search mechanism intact and adds one new component: a hunger-guided Lévy-flight escape operator that targets the most stagnated individuals in the population, tackling premature convergence directly at its source. Testing was carried out on the IEEE 33-bus and 69-bus systems under three settings: single-objective minimization of active power losses, single-objective minimization of annual operating cost, and true multi-objective Pareto optimization combining both criteria. Five other algorithms, HGS, WOA, GO, COA, and FNO, served as benchmarks across multiple independent runs, with Wilcoxon tests used to confirm statistical significance. For the single-objective cases, EHGS produces the lowest mean power loss of all six algorithms (67.59 kW) and the lowest mean annual cost (2,142.4 kUSD), beating the original HGS by a significant margin in both (p = 0.002). When optimizing multiple objectives simultaneously, EHGS reaches the highest hypervolume on the 33-bus system and helds statistically comparable ground against the top performers on the 69-bus system, consistently generating Pareto fronts with strong spread and distribution. To extract a single practical solution from each front, a fuzzy compromise selection strategy is applied to locate balanced trade-off points. One additional finding stood out: in the single-objective setting, competent algorithms tend to converge on nearly the same DG placements regardless of their internal search strategy, a near-saturation effect that highlights why multi-objective coverage carries more practical weight in this kind of planning problem.