FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems
In the dynamic field of optimisation, hybrid algorithms have garnered significant attention for their ability to combine the strengths of multiple methods. This study presents the Hybrid FOX-TSA algorithm, a novel optimisation technique that merges the exploratory capabilities of the FOX algorithm w...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005665 |
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| _version_ | 1841526290089246720 |
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| author | Sirwan A. Aula Tarik A. Rashid |
| author_facet | Sirwan A. Aula Tarik A. Rashid |
| author_sort | Sirwan A. Aula |
| collection | DOAJ |
| description | In the dynamic field of optimisation, hybrid algorithms have garnered significant attention for their ability to combine the strengths of multiple methods. This study presents the Hybrid FOX-TSA algorithm, a novel optimisation technique that merges the exploratory capabilities of the FOX algorithm with the exploitative power of the TSA algorithm. The primary objective is to evaluate the efficiency, robustness, and scalability of this hybrid approach across multiple CEC benchmark suites, including CEC2014, CEC2017, CEC2019, CEC2020, and CEC2022, alongside real-world engineering design problems. The results demonstrate that the Hybrid FOX-TSA algorithm consistently outperforms established optimisation techniques, such as PSO, GWO, and the original FOX and TSA algorithms, in terms of convergence speed, solution quality, and computational efficiency. Notably, the hybrid approach avoids premature convergence and navigating complex search spaces, producing optimal or near-optimal solutions in various test cases. For instance, the algorithm achieved superior performance in minimizing design costs in the Pressure Vessel and Welded Beam Design problems, as well as effectively handling the complex landscapes of the CEC2020 and CEC2022 benchmarks. These results affirm the Hybrid FOX-TSA algorithm as a powerful and adaptable tool for tackling complex optimization problems, particularly in high-dimensional and multimodal landscapes. The integration of statistical analyses, such as t-tests and Wilcoxon signed-rank tests, further supports the statistical significance of its performance improvements. |
| format | Article |
| id | doaj-art-a2887a1569c2415bbc0c658041068caa |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-a2887a1569c2415bbc0c658041068caa2025-01-17T04:49:19ZengElsevierAin Shams Engineering Journal2090-44792025-01-01161103185FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization ProblemsSirwan A. Aula0Tarik A. Rashid1Soran University, Computer Science Department, Soran, Erbil, IraqComputer Science & Engineering Department, Artificial Intelligence & Innovation Centre, University of Kurdistan Hewler, Erbil, Iraq; Corresponding author.In the dynamic field of optimisation, hybrid algorithms have garnered significant attention for their ability to combine the strengths of multiple methods. This study presents the Hybrid FOX-TSA algorithm, a novel optimisation technique that merges the exploratory capabilities of the FOX algorithm with the exploitative power of the TSA algorithm. The primary objective is to evaluate the efficiency, robustness, and scalability of this hybrid approach across multiple CEC benchmark suites, including CEC2014, CEC2017, CEC2019, CEC2020, and CEC2022, alongside real-world engineering design problems. The results demonstrate that the Hybrid FOX-TSA algorithm consistently outperforms established optimisation techniques, such as PSO, GWO, and the original FOX and TSA algorithms, in terms of convergence speed, solution quality, and computational efficiency. Notably, the hybrid approach avoids premature convergence and navigating complex search spaces, producing optimal or near-optimal solutions in various test cases. For instance, the algorithm achieved superior performance in minimizing design costs in the Pressure Vessel and Welded Beam Design problems, as well as effectively handling the complex landscapes of the CEC2020 and CEC2022 benchmarks. These results affirm the Hybrid FOX-TSA algorithm as a powerful and adaptable tool for tackling complex optimization problems, particularly in high-dimensional and multimodal landscapes. The integration of statistical analyses, such as t-tests and Wilcoxon signed-rank tests, further supports the statistical significance of its performance improvements.http://www.sciencedirect.com/science/article/pii/S2090447924005665Multi-objective optimizationHybrid optimizationFOX-TSA AlgorithmCEC benchmark suitesParticle swarm optimizationGrey wolf optimizer |
| spellingShingle | Sirwan A. Aula Tarik A. Rashid FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems Ain Shams Engineering Journal Multi-objective optimization Hybrid optimization FOX-TSA Algorithm CEC benchmark suites Particle swarm optimization Grey wolf optimizer |
| title | FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems |
| title_full | FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems |
| title_fullStr | FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems |
| title_full_unstemmed | FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems |
| title_short | FOX-TSA: Navigating Complex Search Spaces and Superior Performance in Benchmark and Real-World Optimization Problems |
| title_sort | fox tsa navigating complex search spaces and superior performance in benchmark and real world optimization problems |
| topic | Multi-objective optimization Hybrid optimization FOX-TSA Algorithm CEC benchmark suites Particle swarm optimization Grey wolf optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005665 |
| work_keys_str_mv | AT sirwanaaula foxtsanavigatingcomplexsearchspacesandsuperiorperformanceinbenchmarkandrealworldoptimizationproblems AT tarikarashid foxtsanavigatingcomplexsearchspacesandsuperiorperformanceinbenchmarkandrealworldoptimizationproblems |