This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.
In order to solve the deployment problem, which is considered a major issue that faces the design of efficient Wireless Sensor Networks (WSNs), a novel deployment algorithm based on an Enhanced Black Widow Optimization algorithm (EBWO) is proposed. The EBWO algorithm aims to determine the optimal number of sensors and their locations for optimizing both the coverage and the deployment cost. The BWO algorithm is adapted to solve the deployment problem by introducing a set of enhancements, which improves the search capability and the run time of the algorithm. A chaotic initialization is employed in the EBWO algorithm to strengthen the exploration capability of the initial population. Moreover, a modified reproduction mechanism is designed to assist the algorithm in optimizing the number of deployed sensors. Comparisons with modern state-of-the-art deployment methods show that the EBWO algorithm can deliver excellent solutions, where it is ranked first during all the simulations with a coverage difference varying between 3.34% and 7.94% from the other competitors.
In this paper, the problem of deployment in wireless sensor networks is investigated. The authors propose a Hybrid Modified Crow Search Bee Algorithm (HMCSBA) for coverage maximization with the guarantee of connectivity between the deployed sensors. Firstly, a Modified Crow Search Algorithm (MCSA) is proposed based on the basic CSA algorithm to form a connected network after initial random deployment. The position equation of the original CSA was updated by introducing a linear flight length that increases throughout iterations to force the sensors to join the network. Then, the Bees Algorithm (BA) is applied to optimize the network coverage without losing connectivity between the deployed sensors. Simulations and comparative studies were carried out to prove the relevance of the proposed algorithm. Results demonstrate that the proposed algorithm can optimize the coverage and guarantee network connectivity.Povzetek: Razvit je bil hibridni algoritem HMCSBA za zagotovljeno povezljivost brezžičnih senzorjev v omrežju.
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