Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems
This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global expl...
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MDPI AG
2025-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/3/160 |
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| author | Ahmad K. Al Hwaitat Hussam N. Fakhouri Jamal Zraqou Najem Sirhan |
| author_facet | Ahmad K. Al Hwaitat Hussam N. Fakhouri Jamal Zraqou Najem Sirhan |
| author_sort | Ahmad K. Al Hwaitat |
| collection | DOAJ |
| description | This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions—encompassing unimodal, multimodal, and hybrid landscapes—demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges. |
| format | Article |
| id | doaj-art-71bc40d078a54e28b333102ba0370f1b |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-71bc40d078a54e28b333102ba0370f1b2025-08-20T02:41:43ZengMDPI AGAlgorithms1999-48932025-03-0118316010.3390/a18030160Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design ProblemsAhmad K. Al Hwaitat0Hussam N. Fakhouri1Jamal Zraqou2Najem Sirhan3King Abdullah the II IT School, Department of Computer Science, The University of Jordan, Amman 11942, JordanData Science and Artificial Intelligence Department, Faculty of Information Technology, University of Petra, Amman 11196, JordanComputer Science Department, Faculty of Information Technology, University of Petra, Amman 11196, JordanComputer Science Department, Faculty of Information Technology, University of Petra, Amman 11196, JordanThis paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions—encompassing unimodal, multimodal, and hybrid landscapes—demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges.https://www.mdpi.com/1999-4893/18/3/160explorationoptimizationattack-responsesecurityengineering designalgorithms |
| spellingShingle | Ahmad K. Al Hwaitat Hussam N. Fakhouri Jamal Zraqou Najem Sirhan Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems Algorithms exploration optimization attack-response security engineering design algorithms |
| title | Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems |
| title_full | Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems |
| title_fullStr | Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems |
| title_full_unstemmed | Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems |
| title_short | Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems |
| title_sort | hybrid optimization algorithm for solving attack response optimization and engineering design problems |
| topic | exploration optimization attack-response security engineering design algorithms |
| url | https://www.mdpi.com/1999-4893/18/3/160 |
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