Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.

The sailfish optimizer is a recent swarm-intelligence-based optimization algorithm which mimics the hunting behavior of sailfish in the ocean. It consists of two types of populations, namely, sailfish and sardine, where sailfish represent the candidate solutions and sardines freely maneuver in the search space. Existing research studies have shown that the sailfish optimizer suffers from limited global exploration capability, with local optimum stagnation and slow convergence speed. To address these limitations, an improved sailfish optimizer, namely, the Multi-Strategy Sailfish Optimizer, is proposed in this study. This improved version incorporates a modified search strategy for both sailfish and sardines, a non-linear attack power parameter, an improved hunting procedure, and a dynamic sardine population. The impact of all suggested improvements is analyzed experimentally. Several experiments with single-objective problems presented at the Congress on Evolutionary Computation in 2022 are performed to prove the effectiveness and efficiency of the proposed algorithm. This improved algorithm is compared with well-known optimization algorithms, such as the whale optimization algorithm, the sine–cosine algorithm, etc., and improved variants of those algorithms. A convergence behavior analysis is also performed using convergence graphs. Furthermore, the significance of the work is statistically validated. The analysis indicates that the Multi-Strategy Sailfish Optimizer performs significantly better than other optimization algorithms. It is also applied to solve the tension/compression spring design problem and the mobile robot path planning problem.