ArticleBiomimetics (Basel, Switzerland)2026
A Novel Bio-Inspired Multi-Objective Enhanced Honey Formation Optimization Algorithm for Power Systems with Stochastic Renewable Resources.
Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.