This paper proposes a hybrid Beluga Whale Optimization (HBWO) algorithm that integrates Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM) to enhance the performance of the original Beluga Whale Optimization (BWO) algorithm. The HBWO algorithm is designed to improve the convergence speed, avoid local optima, and enhance the search efficiency in solving complex optimization problems. The algorithm is tested on the CEC2017 and CEC2019 test functions, as well as six practical engineering design problems. The experimental results show that HBWO outperforms BWO and other optimization algorithms in terms of solution accuracy and convergence speed. The HBWO algorithm is also effective in solving engineering design problems, demonstrating its practical application value. The key contributions of this paper include the introduction of three strategies to improve the original BWO algorithm, the verification of HBWO's performance on benchmark functions and engineering problems, and the demonstration of HBWO's superiority in solving complex optimization problems. The HBWO algorithm is expected to be a promising approach for solving complex optimization problems in various fields.This paper proposes a hybrid Beluga Whale Optimization (HBWO) algorithm that integrates Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM) to enhance the performance of the original Beluga Whale Optimization (BWO) algorithm. The HBWO algorithm is designed to improve the convergence speed, avoid local optima, and enhance the search efficiency in solving complex optimization problems. The algorithm is tested on the CEC2017 and CEC2019 test functions, as well as six practical engineering design problems. The experimental results show that HBWO outperforms BWO and other optimization algorithms in terms of solution accuracy and convergence speed. The HBWO algorithm is also effective in solving engineering design problems, demonstrating its practical application value. The key contributions of this paper include the introduction of three strategies to improve the original BWO algorithm, the verification of HBWO's performance on benchmark functions and engineering problems, and the demonstration of HBWO's superiority in solving complex optimization problems. The HBWO algorithm is expected to be a promising approach for solving complex optimization problems in various fields.