Search

Abstract In this paper, we consider a strong global search algorithm which exhibits strong exploration ability in unmanned aerial vehicle (UAV)-aided networks. UAVs in wireless communication have aroused great interest recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. Artificial bee colony algorithm is a powerful approach for such a scene. However, due to its one-dimensional and greedy search strategy, it still suffers slow convergence speed. In the traditional version, three types of bees, including employed bees, onlooker bees, and scouts, are employed and they cooperate with each other to find the best food source position. Though different roles, these three types of bees play, there is no difference of division within the internal of each type of bees. Considering this phenomenon, this paper proposes a modified artificial bee colony algorithm with intellective search and special division (ABCIS) to enhance its performance, where different employed bees and different onlooker bees use different search strategies to search for food sources. Besides, the greedy selection method is also abandoned and the food sources’ positions are updated at each iteration. Under this circumstance, the whole population’s experience is fully utilized to guide bee’s search. Finally, to testify the proposed algorithms’ competitiveness, a series of benchmarks are adopted, and the experimental results demonstrate its superior performance among other state-of-the-art algorithm in UAV-aided networks.

Abstract Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm.

Abstract In industrial Internet, many intelligent applications are implemented based on data collection and distribution. Data collection and data distribution in the wireless sensor networks are very important, where the node topology can be described by the spanning tree for obtaining an efficient transmission. Classical algorithms in graph theory such as the Kruskal algorithm or Prim algorithm can only find the minimum spanning tree (MST) in industrial wireless sensor networks. Swarm intelligence algorithm can obtain multiple solutions in one calculation. Multiple solutions are very helpful for improving the reliability of industrial wireless sensor networks. In this paper, we combine quantum computing with artificial bee colony and design a spanning tree construction algorithm for industrial wireless sensor networks. Quantum computations are introduced into the onlooker bees search. Food source replacement strategy is improved. Finally, the algorithm is simulated and evaluated. The results show that the new proposed algorithm can obtain more alternative solutions and has a better performance in search efficiency.