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This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization...
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... A successful meta-heuristic method should incorporate both exploitation and exploration functions while maintaining a proper balance between them for optimal performance (Eiben and Schipper 1998) [78]. Although the natureinspired BWO has demonstrated advantages in solving global optimization problems, it can still suffer from issues such as local optima and unbalanced development, limiting its effectiveness in exploring the entire search space (Chen et al. 2023 [79]; Hussien et al. 2023 [80]). ...
In this paper, a new hybrid meta-heuristic algorithm called CEBWO (cross-entropy method and beluga whale optimization) is presented to solve the mean-CVaR portfolio optimization problem based on jump-diffusion processes. The proposed CEBWO algorithm combines the advantages of the cross-entropy method and beluga whale optimization algorithm with the help of co-evolution technology to enhance the performance of portfolio selection. The method is evaluated on 29 unconstrained benchmark functions from CEC 2017, where its performance is compared against several state-of-the-art algorithms. The results demonstrate the superiority of the hybrid method in terms of solution quality and convergence speed. Finally, Monte Carlo simulation is employed to generate scenario paths based on the jump-diffusion model. Empirical results further confirm the effectiveness of the hybrid meta-heuristic algorithm for mean-CVaR portfolio selection, highlighting its potential for real-world applications.
... BWO is a metaheuristic algorithm that aims to find the global optimal solution among a large number of local optimal solutions. However, the BWO still has the problem of the imbalance between exploration and exploitation and convergence speed [25]. ...
Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.
... NFL's law motivates researchers to enhance their ability to solve new problems by improving currently known algorithms. For example, Chen et al. were inspired by the lifestyle of beluga whales and developed an IBWO [8] that improved the algorithm's global optimization ability; Wen et al. enhanced the global optimization capability of the algorithm by using a new host-switching mechanism [9]; Wu et al. improved the sand cat's wandering strategy and applied it to engineering problems [10]. ...
... ate the population position using Formula (6) ate the value of parameter mop using Formula (9) < 0.5 date the population position using Formula (8) date the population position using Formula (8) ...
The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated.
Reservoir operation exhibits traits of nonlinearity, numerous constraints, and nonconvexity. As the number of reservoirs, such as series and parallel reservoirs, increases, the complexity of the reservoirs also increases. This study solves the complex problem using a new hybrid algorithm (MBWOHHO) based on a modified Beluga whale optimization (BWO) with Harris hawks optimization (HHO). First, in the initialization phase, an opposition-based learning strategy (OBL) is incorporated. This strategy reconstructs the initial spatial position of the population using pairwise comparisons to obtain a higher-quality initial population. Then, a differential mechanism is devised during the global search phase. This strategy enhances global exploration capabilities by cross-combining local optimal individuals with ordinary individuals. Finally, BWO and HHO are organically integrated via a population-based mechanism. This strategy effectively maximizes the strengths of both algorithms while maintaining a balance between exploration and exploitation. Several experiments are conducted across various types and complexities of benchmark functions, including 18 classical and 14 CEC2014 functions. The results of the three cascade reservoir optimization experiments show that MBWOHHO has obvious advantages over the comparison algorithms.
Composite insulators are prone to accelerated aging in coastal and industrially polluted environments, leading to flashovers, power grid outages, and economic losses. Traditional detection methods either require power grid shutdown or lack adequate evaluation capabilities. This research introduces a pixel-level assessment of the aging status of composite insulators using hyperspectral imaging (HSI) technology. And proposed a least squares support vector machine (LSSVM) based on Improved Beluga Whale Optimization (IBWO) algorithm to evaluate the aged levels of composite insulators. First, artificial accelerated aged samples are categorized into aging levels I-VI using the static contact angle. Hyperspectral data in the 400 nm–1040 nm wavelength range are acquired using a HSI device, followed by preprocessing steps involving denoising and dimensionality reduction. Subsequently, the IBWO algorithm is employed to identify the optimal parameters (
C
,
σ
) for LSSVM. The performance of IBWO was compared with other optimization algorithms on test functions, demonstrating that IBWO exhibits the optimal convergence speed and optimization accuracy, effectively enhancing the classification and generalization ability of LSSVM. In this study, the proposed method was compared with other algorithms using k-fold cross validation, resulting in overall accuracy of 96.83% and demonstrating its superior classification capability. The presented method enables pixel-level assessment of the aging degree and visualizes the distribution of aging states in composite insulators. It provides valuable guidance for studying the aging characteristics and structural design in diverse complex environments, holding significant potential for noncontact evaluation of other electrical equipment.
The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation, and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization (MBWO) with a multi-strategy. It was inspired by beluga whales’ two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group aggregation strategy (GAs) and a migration strategy (Ms). The GAs can improve the local development ability of the algorithm and accelerate the overall rate of convergence through the group aggregation fine search; the Ms randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO’s ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.
