Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery

An adaptive deep transfer learning method for bearing fault diagnosis

Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms. For access to material and guide for users of this paper, we host an online page at https://aliasgharheidari.com .

Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance

Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant effect of prior parameters, especially the predefined mode number and balancing parameter, which heavily trouble the traditional VMD. However, parameter-adaptive VMD still encounters some problems when it is applied to the data from industry applications. On one hand, the mode number chosen using parameter-adaptive VMD is not the optimal. Numbers of redundant modes are decomposed. On another hand, parameter-adaptive VMD has much space for the improvement when it is applied to compound-fault diagnosis. To solve these issues and further enhance its performance, an improved parameter-adaptive VMD (IPAVMD) is proposed in this paper. Firstly, a new index, called ensemble kurtosis, is constructed by combining with kurtosis and the envelope spectrum kurtosis. It can simultaneously take the cyclostationary and impulsiveness into consideration. Secondly, the optimization objective function of grasshopper optimization algorithm is improved based on the ensemble kurtosis. The improved method chooses the mean value of the ensemble kurtosis of all modes rather than that of the individual mode as objective function. Thirdly, to extract all potential fault information, an iteration algorithm is used in the new method. Benefiting from these improvements, the proposed IPAVMD outperforms the traditional parameter-adaptive VMD and further expands the application to compound-fault diagnosis. It has been verified by a series of simulated signals and a real dataset from the axle box bearings of locomotive.

Lei Wang

Papers

A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery

Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis

Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary

Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings