Summary Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta‐heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta‐heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta‐heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.

Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group's navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra's algorithms show that HCTNav's memory peak is nine times lower than Dijkstra's in maps with more than 150,000 cells.

Due to the widespread use of communication networks and the ease of transmitting and gathering information through these networks, wireless sensor networks (WSN) have become increasingly popular. Usability in any environment without the need for environmental monitoring and engineering of these networks has led to their increasing usage in various fields. Routing information from the sensor node to sink, so that node energy is consumed uniformly and network life is not reduced, is one of the most important challenges in wireless sensor networks. Most wireless networks have no infrastructure, and embedded sensor nodes have limited power. Thus, the early termination of the wireless node’s energy based on the transmission of messages over the network can disrupt the entire network process. In this paper, the object is designed to find the optimal path in WSN based on the multiobjective greedy approach to the near optimal path. The proposed model is presented in this method to transfer sensed data of the sensor network to the base station for the desired applications. In this method, the sensor nodes are identified as adjacent nodes based on their distance. The energy of all nodes initially is approximately equal, which decreases with the transfer of information between the nodes. In this way, when a node senses a message, it checks several factors for transmitting information to its adjacent nodes and selects the node with the largest amount of factors as the next hop. The simulation results show that the energy consumption in the network grids is almost symmetrically presented, and the network lifetime is reduced with a gentle slope that provides optimum energy consumption in the networks. Also, the packet transmission delay in the network reaches 450 milliseconds for the transmission of information between 15 nodes and 650 connections. Besides, network throughput increases by approximately 97%. It also shows better performance compared to other previous methods in terms of evaluation criteria.

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With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.