Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach’s effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.

This study proposes a new classifying approach for identifying collected failure data of cluster head (CH) in wireless sensor networks (WSN) based on hybridizing improved multi-verse optimizer (MVO) and feedforward neural network (FNN). An improvement of the MVO is proposed based on enhancing diversity agents for avoiding it’s disadvanced of the local optimal. The data failure is detected for aggregating data in CH to forward to the base station (BS) based on classification by applying hybrid improved MVO and FNN. Twelve selected benchmark functions and the problem of identifying failure data in WSN are used in conducting comprehensive experiments to evaluate the performance of the proposed method. The experimental results are investigated and compared with the other approaches in the literature. The compared result exhibits the proposed technique that provides the alternative tool with the anticipation of influence on data sets and an effective way of forwarding the correct data from CH to BS in WSN applications.

Recent advancements in digital systems and internet technology have raised concerns about information security and privacy. Information hiding techniques (IHTs), such as steganography, watermarking, and covert channels, play a crucial role in protecting sensitive data from unauthorized access. This research review paper provides a comprehensive overview of the recent developments in information hiding techniques via internet technology. The paper discusses methodologies, algorithms, and tools used to conceal information within digital media, network protocols, and web applications. It also explores the challenges and future directions in this field, highlighting the need for enhanced security measures and privacy protection. This review serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding and advancing information hiding techniques in the digital era over the internet.