Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the critical need for sustainable energy solutions. Microgrids (MGs) provide practical applications for renewable energy, reducing reliance on fossil fuels and mitigating ecological impacts. However, renewable energy poses reliability challenges due to its intermittency, primarily influenced by weather conditions. Additionally, fluctuations in fuel prices and the management of multiple devices contribute to the increasing complexity of MGs and the necessity to address a range of objectives. These factors make the optimization of Energy Management Strategies (EMSs) essential and necessary. This study contributes to the field by categorizing the main aspects of MGs and optimization EMS, analyzing the impacts of weather on MG performance, and evaluating their effectiveness in handling multi-objective optimization and data considerations. Furthermore, it examines the pros and cons of different methodologies, offering a thorough overview of current trends and recommendations. This study serves as a foundational resource for future research aimed at refining optimization EMS by identifying research gaps, thereby informing researchers, practitioners, and policymakers.

Speed controllers may be employed to provide safer along with more secure vehicles. They may also be used to minimise environmental pollution, e.g. speed controllers can be employed to track speed optimal velocity profile based on energy consumption minimisation. Consequently, accurate speed tracking is important. However, despite soft‐computing techniques have been proved successful in controller tuning, there is a limited amount of research on these techniques applied to speed controller optimisation. Therefore, this study performs a comparison study on PI cruise controller tuning for an off‐road electric vehicle. A cost function is designed to reach an accurate EV speed tracking while considering safety aspects, such as no reverse speed. The ACO‐NM algorithm has been demonstrated to be the most efficient compared to GA, ALO, DE, and PSO. Indeed, ACO‐NM reached high‐quality solutions for lower computational cost for three driving cycles. Moreover, contrary to the majority of published work on the subject, experimental validations have been carried out with the optimised PI cruise controllers. The experimental results have validated the ACO‐NM efficiency with a maximum overshoot average <10% for the hardest acceleration of the real driving cycle.

Over the last decades, researchers have studied the Multi-Objective Optimization problem (MOO) for Multi-Agent Systems (MASs). However, most of them consider the problem formulation to be an unprioritized sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of objective functions to facilitate the study of the subject and identify the needs arising from it. In the context of Multi-Robot Systems (MRSs), most studies only focus on the mathematical development of their proposed MOO algorithm without paying attention to the application. In practice, there is not a comprehensive review to identify the reliable algorithms already applied to real platforms. Using a mapping and state-of-the-art review, this paper aims to fill these gaps by first offering a detailed overview of the discrete-time MOO methods for MASs. More specifically, we classify existing MOO methods based on the formulation of the problem into the sum of objective functions and the prioritized sum of objective functions. Secondly, we review the applications of these methods in MRSs and the practical implementation of MOO algorithms on real MRS. Finally, we suggest future research directions to extend the existing methods to more realistic approaches, including open problems in the new research area of the prioritized sum of objective functions and practical challenges for the implementation of the existing methods in robotics. This work introduces the field of MAS to researchers and enables them to position themselves in the current research trends.