Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance
Abstract This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET), which is a novel optimization framework designed to enhance the efficiency and robustness of multi-robot exploration. AMET combines the deterministic structure of Coordinated Multi-Robot Exp...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08194-w |
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| author | Ali El Romeh Seyedali Mirjalili |
| author_facet | Ali El Romeh Seyedali Mirjalili |
| author_sort | Ali El Romeh |
| collection | DOAJ |
| description | Abstract This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET), which is a novel optimization framework designed to enhance the efficiency and robustness of multi-robot exploration. AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. To validate its effectiveness, AMET is compared to both multi-objective and single-objective exploration strategies, including CME combined with Multi-Objective Grey Wolf Optimizer (CME-MGWO), Multi-Objective Ant Colony Optimization (CME-MACO), Multi-Objective Dragonfly Algorithm (CME-MODA), and the single-objective CME with traditional Salp Swarm Algorithm (CME-SSA). The evaluation focuses on four critical performance metrics: runtime efficiency, exploration area coverage, mission completion resilience, and the reduction of redundant exploration. Experimental results across multiple case studies demonstrate that AMET consistently outperforms both single-objective and multi-objective counterparts, achieving superior area coverage, reduced computational overhead, and enhanced exploration coordination. These findings highlight the potential of AMET as a scalable and efficient approach for robotic exploration, providing a foundation for future advancements in multi-robot systems. The proposed method opens new possibilities for applications in search-and-rescue operations, planetary surface exploration, and large-scale environmental monitoring. |
| format | Article |
| id | doaj-art-6fdfe0b90e2446b185c5d866f5b5fcbf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6fdfe0b90e2446b185c5d866f5b5fcbf2025-08-20T03:45:53ZengNature PortfolioScientific Reports2045-23222025-07-0115113310.1038/s41598-025-08194-wMulti robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performanceAli El Romeh0Seyedali Mirjalili1Centre for Artificial Intelligence Research and Optimization, Torrens University AustraliaCentre for Artificial Intelligence Research and Optimization, Torrens University AustraliaAbstract This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET), which is a novel optimization framework designed to enhance the efficiency and robustness of multi-robot exploration. AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. To validate its effectiveness, AMET is compared to both multi-objective and single-objective exploration strategies, including CME combined with Multi-Objective Grey Wolf Optimizer (CME-MGWO), Multi-Objective Ant Colony Optimization (CME-MACO), Multi-Objective Dragonfly Algorithm (CME-MODA), and the single-objective CME with traditional Salp Swarm Algorithm (CME-SSA). The evaluation focuses on four critical performance metrics: runtime efficiency, exploration area coverage, mission completion resilience, and the reduction of redundant exploration. Experimental results across multiple case studies demonstrate that AMET consistently outperforms both single-objective and multi-objective counterparts, achieving superior area coverage, reduced computational overhead, and enhanced exploration coordination. These findings highlight the potential of AMET as a scalable and efficient approach for robotic exploration, providing a foundation for future advancements in multi-robot systems. The proposed method opens new possibilities for applications in search-and-rescue operations, planetary surface exploration, and large-scale environmental monitoring.https://doi.org/10.1038/s41598-025-08194-wMulti-robot explorationSalp swarm algorithmMulti-objective optimizationArtificial IntelligenceComputational efficiencyArea coverage |
| spellingShingle | Ali El Romeh Seyedali Mirjalili Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance Scientific Reports Multi-robot exploration Salp swarm algorithm Multi-objective optimization Artificial Intelligence Computational efficiency Area coverage |
| title | Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance |
| title_full | Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance |
| title_fullStr | Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance |
| title_full_unstemmed | Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance |
| title_short | Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance |
| title_sort | multi robot exploration using an advanced multi objective salp swarm algorithm for efficient coverage and performance |
| topic | Multi-robot exploration Salp swarm algorithm Multi-objective optimization Artificial Intelligence Computational efficiency Area coverage |
| url | https://doi.org/10.1038/s41598-025-08194-w |
| work_keys_str_mv | AT alielromeh multirobotexplorationusinganadvancedmultiobjectivesalpswarmalgorithmforefficientcoverageandperformance AT seyedalimirjalili multirobotexplorationusinganadvancedmultiobjectivesalpswarmalgorithmforefficientcoverageandperformance |