Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms