A multi-strategy improved beluga whale optimization algorithm for constrained engineering problems

Beluga whale optimization (BWO) has received widespread attention in scientific and engineering domains. However, BWO suffers from limited adaptability, weak anti-stagnation, and poor exploration capability. Consequently, this study proposes an enhanced variant of BWO called multi-strategy improved beluga whale optimization (MIBWO). First, an improved ICMIC chaotic map is introduced to enhance exploration capability and optimization accuracy. Then, a dynamic parameter nonlinear adjustment strategy is integrated to achieve a better balance between exploration and exploitation. Finally, opposition learning based on the lens imaging principle is designed to strengthen anti-stagnation capability. An ablation experiment is performed to evaluate the impact of each strategy on the optimization capability of BWO. The experimental results demonstrate the significant enhancement in the performance of BWO owing to the used strategies. To further validate the performance of MIBWO, it is benchmarked against six state-of-the-art optimization algorithms using functions from CEC2005, CEC2014, and CEC2022. Statistical tests, including Friedman rank test and Wilcoxon rank-sum test, are performed. The experimental results show the superiority of MIBWO. Finally, MIBWO is applied to optimize 2D and 3D node coverage in wireless sensor networks and solve six constrained engineering problems. The experimental results indicate that MIBWO outperforms other competitors for practical engineering applications in terms of solution quality and convergence speed.