ArticleScientific reports2025
Multi-objective optimization of hybrid energy systems using gravitational search algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Based Intelligent Resource Management for Renewable Energy Engineering Projects.Annals of the New York Academy of Sciences · 2026Article
- Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems.MethodsX · 2025Article
- Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks.Scientific reports · 2025Article
- Optimal sizing of hybrid renewable energy systems relying on the black winged kite algorithm for performance evaluation.Scientific reports · 2025Article
- Ascertaining sustainability for affordable energy generation with non-renewable sources using computational intelligence algorithm.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO
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Registered trials
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