Enhancing benchmark optimization with evolutionary random approach: A comparative analysis of modified adaptive bats sonar algorithm (MABSA)
Recently, evolutionary algorithms have emerged as powerful tools for solving complex optimization problems across various domains. This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its p...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
Springer Science and Business Media Deutschland GmbH
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/42179/1/Enhancing%20Benchmark%20Optimization%20with%20Evolutionary.pdf https://umpir.ump.edu.my/id/eprint/42179/7/Enhancing%20benchmark%20optimization%20with%20evolutionary%20random%20approach.pdf https://umpir.ump.edu.my/id/eprint/42179/ https://doi.org/10.1007/978-981-96-5690-5_10 |
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| Summary: | Recently, evolutionary algorithms have emerged as powerful tools for solving complex optimization problems across various domains. This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its performance with the original MABSA. The hybrids approach, termed as MABSA-SSA, integrates the adaptive echolocation mechanism of MABSA with the dynamic foraging strategy of SSA to enhance exploration and exploitation capabilities. The MABSA-SSA aims to capitalize on the strengths of both constituent algorithms. MABSA's adaptive frequency and loudness adjustments are complemented by SSA's ability to dynamically balance global and local searches through its seasonal foraging behavior. This synergistic approach is designed to improve convergence speed and solution accuracy, particularly in high-dimensional and multimodal optimization problems. Experimental evaluations are conducted using a comprehensive suite of seven benchmark functions to assess the performance of MABSA-SSA against the original MABSA. The results demonstrate that MABSA-SSA consistently better solution quality compared to MABSA alone. Notably, the hybrid algorithm exhibits superior performance in avoiding local optima and maintaining solution diversity. Statistical analysis confirms the significant improvements offered by the MABSA-SSA combination. These findings suggest that the hybrid algorithm provides a more robust and efficient optimization framework, suitable for a wide range of applications. As conclusion, the MABSA-SSA hybrid algorithm represents a significant advancement in benchmark optimization. Its superior performance compared to the original MABSA underscores the potential benefits of algorithmic hybridization in addressing complex optimization challenges. |
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