An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems
The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/5/441 |
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| author | Yaoyao Lin Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang |
| author_facet | Yaoyao Lin Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang |
| author_sort | Yaoyao Lin |
| collection | DOAJ |
| description | The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm’s exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method. |
| first_indexed | 2024-03-10T22:59:56Z |
| format | Article |
| id | doaj.art-087e001b38e245cc9c48d68105419b9e |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T22:59:56Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-087e001b38e245cc9c48d68105419b9e2023-11-19T09:44:33ZengMDPI AGBiomimetics2313-76732023-09-018544110.3390/biomimetics8050441An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization ProblemsYaoyao Lin0Ali Asghar Heidari1Shuihua Wang2Huiling Chen3Yudong Zhang4Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKThe Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm’s exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.https://www.mdpi.com/2313-7673/8/5/441Hunger Games Searchswarm intelligencelogarithmic spiralRosenbrock Methodbenchmarkengineering optimization problems |
| spellingShingle | Yaoyao Lin Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems Biomimetics Hunger Games Search swarm intelligence logarithmic spiral Rosenbrock Method benchmark engineering optimization problems |
| title | An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems |
| title_full | An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems |
| title_fullStr | An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems |
| title_full_unstemmed | An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems |
| title_short | An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems |
| title_sort | enhanced hunger games search optimization with application to constrained engineering optimization problems |
| topic | Hunger Games Search swarm intelligence logarithmic spiral Rosenbrock Method benchmark engineering optimization problems |
| url | https://www.mdpi.com/2313-7673/8/5/441 |
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