A Novel Bio-Inspired Multi-Objective Enhanced Honey Formation Optimization Algorithm for Power Systems with Stochastic Renewable Resources

The increasing prevalence of renewable energy sources (RESs), along with the uncertainty in their generation characteristics and conflicting economic, environmental, and technical objectives, has significantly increased the complexity of Economic–Environmental–Technical Dispatch (EETD) problems. Although numerous studies have been proposed in the literature for optimal power flow (OPF) and Economic Emission Dispatch (EED), comprehensive multi-objective EETD studies that account for stochastic renewable generation remain limited. Many existing Pareto-based approaches still exhibit insufficient convergence, poor solution diversity, and low-quality compromise solutions. To address these limitations, this paper proposes the Multi-Objective Enhanced Honey Formation Optimization (MO-EHFO) algorithm for EETD with integrated renewable energy. While preserving the five-phase basic structure of the original HFO, the MO-EHFO algorithm integrates archive-based, chaotic, and dynamic selective improvements for each phase to optimize population diversity, search capability, and convergence performance, using a Pareto-consistent approach. Evaluations conducted on a modified IEEE 30-bus system across nine case studies, each involving two, three, or four objectives, validate the exceptional performance of MO-EHFO. Additionally, the robustness of the proposed algorithm was tested using 25 simplified renewable energy-based scenarios representing specific times of the year. Simulation results show that while MO-EHFO satisfies all operational and safety constraints, it consistently produces superior Pareto fronts and high-quality compromise solutions compared to MO-HFO, NSGA-II, and the current literature. In conclusion, this study demonstrates that MO-EHFO is a reliable decision-support tool that provides users with the optimal balance between practicality, cost, and technical safety in complex and uncertain power system problems.