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Optimization and design of mobile wireless sensor networks (MWSNs) must assure adequate spatial coverage of the site. The spatial coverage optimization aims to enrich discoverability of MWSN by specifying mobile sensors geographical locations in order to maximize their coverage. In this paper, an enhanced metaheuristic algorithm called “firefly algorithm with crossover and detection phases” is introduced for optimizing the area coverage percentage of MWSN. The proposed algorithm is tested on many datasets with different criterions and compared with other algorithms including differential evolution, whale optimization algorithm, and flower pollination algorithm. The experimental results are analysed with one‐way ANOVA test. In addition, the proposed algorithm is compared with particle swarm optimization, and the results are analysed with Wilcoxon signed‐rank test. The overall analysis results prove the prosperity and efficient exploration of the proposed algorithm.

The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional twodimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation.INDEX TERMS 6G technology, computational intelligence, non-terrestrial base stations, reconfigurable intelligent surfaces, three-dimensional coverage optimization problem.

As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy; However, HSOA could be more stable. The source code of this study is publicly available at https://drive.matlab.com/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69.