Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation
In this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10818694/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841557035243536384 |
|---|---|
| author | Mohammed Omari Mohammed Kaddi Khouloud Salameh Ali Alnoman Mohammed Benhadji |
| author_facet | Mohammed Omari Mohammed Kaddi Khouloud Salameh Ali Alnoman Mohammed Benhadji |
| author_sort | Mohammed Omari |
| collection | DOAJ |
| description | In this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed in atoms, particularly during processes involving electrostatic charge and discharge. Each solution in AEO is represented as an atom with electrons, where its energy state corresponds to solution quality. Solutions interact, exchanging and dissipating energy in a manner analogous to static electricity buildup and release—such as when a plastic rod is rubbed to accumulate charge, then gradually neutralized upon contact with another surface. This energy-driven model allows AEO to balance exploration and exploitation dynamically, enhancing its ability to navigate com-plex, multimodal landscapes. The paper delves into key AEO mechanisms, including energy transfer, dissipation, and parameter sensitivity, exploring their impact on the algorithm’s performance across challenging benchmarks. Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems. |
| format | Article |
| id | doaj-art-ac1b0386c25a47c98444116aa2b7168f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ac1b0386c25a47c98444116aa2b7168f2025-01-07T00:02:25ZengIEEEIEEE Access2169-35362025-01-01132801282810.1109/ACCESS.2024.352432210818694Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and DissipationMohammed Omari0https://orcid.org/0000-0002-6218-5560Mohammed Kaddi1https://orcid.org/0000-0003-0903-4218Khouloud Salameh2https://orcid.org/0000-0003-0620-7769Ali Alnoman3Mohammed Benhadji4https://orcid.org/0009-0003-5655-060XComputer Science and Engineering Department, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab EmiratesLDDI Laboratory, Faculty of MSMCS, University of Ahmed Draia Adrar, Adrar, AlgeriaComputer Science and Engineering Department, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab EmiratesComputer Science and Engineering Department, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab EmiratesMaterial Sciences Department, LEESI Laboratory, University of Ahmed Draia Adrar, Adrar, AlgeriaIn this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed in atoms, particularly during processes involving electrostatic charge and discharge. Each solution in AEO is represented as an atom with electrons, where its energy state corresponds to solution quality. Solutions interact, exchanging and dissipating energy in a manner analogous to static electricity buildup and release—such as when a plastic rod is rubbed to accumulate charge, then gradually neutralized upon contact with another surface. This energy-driven model allows AEO to balance exploration and exploitation dynamically, enhancing its ability to navigate com-plex, multimodal landscapes. The paper delves into key AEO mechanisms, including energy transfer, dissipation, and parameter sensitivity, exploring their impact on the algorithm’s performance across challenging benchmarks. Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems.https://ieeexplore.ieee.org/document/10818694/Atomic energy optimizationmeta-heuristic optimizationstatic electricity dissipationenergy-based optimization modelsRastrigin functiontraveling salesman problem |
| spellingShingle | Mohammed Omari Mohammed Kaddi Khouloud Salameh Ali Alnoman Mohammed Benhadji Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation IEEE Access Atomic energy optimization meta-heuristic optimization static electricity dissipation energy-based optimization models Rastrigin function traveling salesman problem |
| title | Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation |
| title_full | Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation |
| title_fullStr | Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation |
| title_full_unstemmed | Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation |
| title_short | Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation |
| title_sort | atomic energy optimization a novel meta heuristic inspired by energy dynamics and dissipation |
| topic | Atomic energy optimization meta-heuristic optimization static electricity dissipation energy-based optimization models Rastrigin function traveling salesman problem |
| url | https://ieeexplore.ieee.org/document/10818694/ |
| work_keys_str_mv | AT mohammedomari atomicenergyoptimizationanovelmetaheuristicinspiredbyenergydynamicsanddissipation AT mohammedkaddi atomicenergyoptimizationanovelmetaheuristicinspiredbyenergydynamicsanddissipation AT khouloudsalameh atomicenergyoptimizationanovelmetaheuristicinspiredbyenergydynamicsanddissipation AT alialnoman atomicenergyoptimizationanovelmetaheuristicinspiredbyenergydynamicsanddissipation AT mohammedbenhadji atomicenergyoptimizationanovelmetaheuristicinspiredbyenergydynamicsanddissipation |