The actual state of a multi-robots system is then introduced as feedback information to eliminate formation error. In this method, the problem of seeking for time optimal control law is converted into a parameter optimization problem by control parameterization and time discretization, so that the control law can be derived with BA. This paper proposes a Bat Algorithm (BA) based Control Parameterization and Time Discretization (BA-CPTD) method to acquire time optimal control law for formation reconfiguration of multi-robots system. We compared ARBA with the other algorithms in this field the experimental results demonstrate that ARBA exhibits better performance in multirobot target searching and can be applied to multirobot intelligent systems. Experiments were conducted in three aspects to verify the effectiveness and efficiency of ARBA. Moreover, the location of the target in an unknown environment is unknown, and a multi-swarm strategy is introduced into the ARBA to improve the diversity and expand the search space of robots so that robots can find the location of the target as well as the target itself faster than the existing algorithms. In addition, the Doppler effect is introduced to improve ARBA the effect can be adaptively compensated when the robot moves and helps robots avoid premature convergence. The adaptive inertial weight strategy helps ARBA improve its diversity and provides an effective mechanism for escaping from local optima. The obstacle avoidance problem is considered in the proposed ARBA. In this paper, an improved bat algorithm (BA) for multirobot target searching in unknown environments, named adaptive robotic bat algorithm (ARBA), is proposed it acts as the controlling mechanism for robots. Multirobot target searching in unknown environments is a currently trending topic of discussion. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The open-source codes of BA code are given to build a wealthy BA review. The critical analysis of the limitations and shortcomings of BA is also mentioned. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. The ecosystem of bat animals inspires the main idea of BA. Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains.
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