An Improved Adaptive Whale Optimization Algorithm with Multi-Strategy for Optimization Problems
The Whale Optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems. However, like many metaheuristic algorithms, it has its limitations. Insufficient population diversity and ineffective search strategies are common...
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| Vydáno v: | 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE) s. 240 - 244 |
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IEEE
28.02.2025
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| Abstract | The Whale Optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems. However, like many metaheuristic algorithms, it has its limitations. Insufficient population diversity and ineffective search strategies are common challenges that can reduce its performance. We urgently need to introduce effective strategies to alleviate these core shortcomings of WOA. To address the slow convergence, low accuracy, and local optima issues of WOA, this paper proposes an Improved Adaptive Whale Optimization Algorithm with multi-strategy(IWOA). Firstly, enhanced Sine chaotic map was introduced to improve the quality of the initial population. Secondly, the adaptive inertia weights are used to balance the global and local searches, so as to accelerate the convergence of the algorithm. Lastly, the Metropolis criterion is incorporated to strengthen the algorithm's ability to escape local optima. Datasets are used to verify the performance of the improved adaptive whale optimization algorithm in solving problems. |
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| AbstractList | The Whale Optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems. However, like many metaheuristic algorithms, it has its limitations. Insufficient population diversity and ineffective search strategies are common challenges that can reduce its performance. We urgently need to introduce effective strategies to alleviate these core shortcomings of WOA. To address the slow convergence, low accuracy, and local optima issues of WOA, this paper proposes an Improved Adaptive Whale Optimization Algorithm with multi-strategy(IWOA). Firstly, enhanced Sine chaotic map was introduced to improve the quality of the initial population. Secondly, the adaptive inertia weights are used to balance the global and local searches, so as to accelerate the convergence of the algorithm. Lastly, the Metropolis criterion is incorporated to strengthen the algorithm's ability to escape local optima. Datasets are used to verify the performance of the improved adaptive whale optimization algorithm in solving problems. |
| Author | Zhu, Kanghua Zhang, Jiarui Zhi, Yunchao Liu, Qunfeng |
| Author_xml | – sequence: 1 givenname: Jiarui surname: Zhang fullname: Zhang, Jiarui organization: Dongguan University of Technology,Department of Computer Science and Technology – sequence: 2 givenname: Qunfeng surname: Liu fullname: Liu, Qunfeng email: qfliu@dgut.edu.cn organization: Dongguan University of Technology,Department of Computer Science and Technology – sequence: 3 givenname: Yunchao surname: Zhi fullname: Zhi, Yunchao organization: Dongguan University of Technology,Department of Computer Science and Technology – sequence: 4 givenname: Kanghua surname: Zhu fullname: Zhu, Kanghua organization: Dongguan University of Technology,Department of Computer Science and Technology |
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| Snippet | The Whale Optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems.... |
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| SubjectTerms | Accuracy adaptive inertia weight chaotic map Consumer electronics Convergence Flowcharts Heuristic algorithms Metaheuristics Metropolis criterion NP-hard problem Search problems Simulated annealing Whale Optimization Algorithm Whale optimization algorithms |
| Title | An Improved Adaptive Whale Optimization Algorithm with Multi-Strategy for Optimization Problems |
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