# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
###### Abstract  
The whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm developed by [PERSON] and [PERSON] in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, scholars have adopted this algorithm to address various problems in different disciplines. However, standard WOA has the problems of slow convergence speed, insufficient search accuracy, and limited ability to solve complex problems. In order to solve these problems, this paper proposes a multi-strategy hybrid whale algorithm (MHWOA). Firstly, the calculation speed is accelerated by modifying the parameters; then, the accuracy of the algorithm is improved by incorporating the scatter search strategy; finally, the simulated annealing algorithm is integrated to improve its ability to solve complex problems. The performance differences between MHWOA, the baseline algorithm, and the improved WOA algorithm are compared using the CEC2017 test suite and three real-world engineering problems. In the comparison of processing results of various problems, the calculation accuracy of MHWOA is improved by no less than 1.96%, the calculation error is reduced by no less than 1.83%, and the execution time is improved by no less than 5.6%. In the CNN-MHWOA-based time series electricity load forecasting problem, MHWOA shows the advantages of reduced error and improved fitting degree with the true value compared with the standard WOA. w +
Footnote †: journal: applied sciences  
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Footnote †: journal: applied sciences  
Metaheuristic algorithms can be divided into three types: evolutionary, physics-based, and swarm-based methods [12]. Among them, the swarm intelligence algorithm is a more widely used and developed algorithm type. Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm developed by [PERSON] and [PERSON] in 2016. The advantage of WOA is that its algorithm structure is simple, and compared with most swarm intelligence algorithms, WOA contains fewer parameters. This makes it more computationally convenient. In terms of processing problems, the range of dimensions that WOA can map is unlimited, and it has multidimensional decision boundaries. This also shows that WOA can be easily applied to handle high-dimensional complex problems [13]. Due to its superiority, WOA has been improved by researchers, employing different techniques to enhance its performance. Conversely, WOA also has some drawbacks. Since most of the parameters of WOA depend on random numbers in the process of participating in the iteration, it is easy to cause the problem of low solution accuracy and insufficient stability in the calculation of WOA. In addition, there is no appropriate evolutionary mechanism in the search strategy of WOA to drive individuals to update, which makes it easy for the population to fall into local optimum [14]. The current improvements of WOA can be summarized into three categories: variant-based WOA, enhancement-based WOA, and hybrid-based WOA, as shown in Figure 1.  
On the modification of the variant WOA, [PERSON] et al. [15] proposed the binary whale optimization algorithm (BWO). By combining binary and WOA, the complex profit problem of a power company is calculated. This provides a reference for the application of WOA to computationally complex problems. [PERSON] et al. [16] proposed memetic fuzzy whale optimization (MFWO). By combining fuzzy clustering and WOA, WOA is suitable for medical image processing. [PERSON] et al. [17] proposed multi-objective whale optimization to apply WOA in the field of multi-objective optimization. The above improvements apply WOA to different domains and problems; such improvements are good at computing a specific class of problems. These improvements are usually specific to a particular problem, and the WOA is specialized to solve the problem.