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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Bibliographic Details
Published in:2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE) pp. 240 - 244
Main Authors: Zhang, Jiarui, Liu, Qunfeng, Zhi, Yunchao, Zhu, Kanghua
Format: Conference Proceeding
Language:English
Published: IEEE 28.02.2025
Subjects:
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
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Summary: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.
DOI:10.1109/ICCECE65250.2025.10985420