The Bald Eagle Search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration and exploitation phases. To address these limitations, this paper introduces the Multi-Strategy Boosted Bald Eagle Search (MBBES) algorithm. MBBES enhances the original BES by incorporating an adaptive parameter, two distinct mutation strategies, and replacing the swoop stage with a fall stage. We rigorously evaluate MBBES against classic and improved algorithms using the CEC2014 and CEC2017 test sets. The experimental results demonstrate that MBBES significantly improves the ability to escape local optima and achieves superior convergence accuracy. Moreover, MBBES ranks first according to the Friedman test, outperforming its counterparts in solving five practical engineering problems and three MLP classification problems, underscoring its effectiveness in real-world optimization scenarios. These findings indicate that MBBES not only surpasses BES but also sets a new benchmark in optimization performance.

Recently, there has been an increasing advancement in the communications technology, and due to the increment in using the cellphone applications in the diverse aspects of life, it became possible to automate home appliances, which is the desired goal from residences worldwide, since that provides lots of comfort by knowing that their appliances are working in their highest efficiency whenever it is required without their knowledge, and it also allows them to control the devices when they are away from home, including turning them on or off whenever required. The design and implementation of this system is carried out by using the Global System of Mobile communications (GSM) technique to control the home appliances - In this work, an electric fan is used, and a sounding buzzer is used as well - and that is through the utilization of Short Message Services (SMS). Besides that, an Arduino board has been integrated with a number of sensors such as a motion sensor, a temperature sensor, and a screen for displaying required information to the user which indicates whatever the system is doing, where this data includes the state of appliances, where the Arduino board can control the home appliances through the relay board that accepts low power signals, and based upon them, it activates high power devices.

In recent years, reinforcement learning (RL) has garnered a great deal of interest from researchers because of its success in handling some complicated issues. Specifically, Q-learning as a model of RL is used a lot in various fields, and it has given an attractive result in games. In recent years, some researchers have tried to exploit the power of Q-learning to improve the results of optimization algorithms by guiding the optimization algorithm search agents based on the data saved in Q-table during the search process. The best search agent is chosen based on its accumulated performance, in other words, how well it has done overall, not how well it has done at each iteration. It is important to note that this review does not focus on reinforcement learning algorithms collaborating with metaheuristic optimization algorithms because there are so many reinforcement learning algorithms and to narrow the scope of the review, this paper will only discuss Q-learning used to enhance metaheuristic optimization algorithms. In this study will look at the huge progress made in the research community by looking at 32 different algorithms proposed on the subject from 2009 to 2022, with a focus on studies published in the last five years. As a result of the surveys conducted in this study, researchers (novices and experts) in the field of metaheuristic optimization algorithms research are expected to gain a better understanding of current research trends involving the use of Q-Learning and new motivations for outlining appropriate strategic plans for future development work as a result of the surveys conducted in this study.