A hybrid antlion and hunter prey optimization paradigm for optimal allocation of distributed generation in radial distribution system

Optimal selection of distributed generation (DG) locations and sizes is essential for improving the performance of radial distribution systems (RDSs). This paper proposes a hybrid Ant Lion Optimizer–Hunter-Prey Optimizer (ALO–HPO) for optimal DG placement and sizing, combining the global search capability of ALO with the local refinement characteristics of HPO to achieve an improved exploration–exploitation balance. The proposed approach is formulated for both single-objective and multi-objective DG allocation, considering real power loss (RPL), voltage deviation, and voltage stability. Loss sensitivity factor (LSF) analysis is employed to identify candidate buses, thereby reducing the search space for DG placement. The proposed method is evaluated on the IEEE 33-bus RDS under static-load conditions, with additional assessment under 24-h time-varying load and peak-demand conditions. The investigation is further extended to the IEEE 136-bus RDS to demonstrate its robustness for large-scale power networks. The simulation results demonstrate that the hybrid ALO–HPO consistently reduces RPL while improving voltage profiles. For a single-objective DG allocation study, the hybrid ALO–HPO achieved an RPL reduction of 70.97% and 83.76% with the optimized inclusion of Type-I and Type-III DGs in the 33-bus RDS under static-load conditions. Comparatively, the ALO and HPO algorithms delivered only 66.60% and 68.22% power loss reductions for Type-I DGs and 71.39% and 78.14% for Type-III DGs, respectively, with optimal connections. Further, the hybrid algorithm converged in less computational iteration and required less computational time than the ALO and HPO. Similar performance was observed under time-varying load and peak-demand conditions, demonstrating its applicability to DG planning across different operating conditions.