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.
Abstract-The problem in the autonomous navigation of a mobile robot is to define a strategy that allows it to reach the final destination and avoiding obstacles. Fuzzy logic is considered as an important tool to solve this problem. It can mimic reasoning abilities of the human being in navigation tasks. However a major problem of fuzzy systems is obtaining their parameters which are generally specified by human experts. This process can be long and complex. In order to generate optimal parameters of fuzzy controller, this work propose a learning and optimization process based on ant colony algorithm ACO and genetic algorithm operators (crossover and mutation).We present a comparison between inference system for autonomous navigation based on fuzzy logic before and after learning. The simulated results show clearly the impact of the optimization approach improves the fuzzy controller performance mainly in obstacle avoidance and detection of the shortest path.
In this article, two hybrid schemes using the Bees Algorithm (BA) and the Firefly Algorithm (FA) are presented for numerical complex problem resolution. The BA is a recent population-based optimization algorithm, which tries to imitate the natural behaviour of honey bees foraging for food. The FA is a swarm intelligence technique based upon the communication behaviour and the idealized flashing features of tropical fireflies. The first approach, called the Hybrid Bee Firefly Algorithm (HBAFA), centres on improvements to the BA with FA during the local search thus increasing exploitation in each research zone. The second one, namely the Hybrid Firefly Bee Algorithm (HFBA), uses FA in the initialization step for a best exploration and detection of promising areas in research space. The performance of the novel hybrid algorithms was investigated on a set of various benchmarks and compared with standard BA, and other methods found in the literature. The results show that the proposed algorithms perform better than the Standard BA, and confirm their effectiveness in solving continuous optimization functions.
Abstract-This paper proposes a hybrid approach based on limit-cycles method and fuzzy logic controller for the problem of obstacle avoidance of mobile robots in unknown environment. The purpose of hybridization consists on the improvement of basic limit-cycle method in order to obtain safe and flexible navigation. The proposed algorithm has been successfully tested in different configurations on simulation.
Recently, OSTBCs has become a widespread technique for signal transmission over wireless channels because of their diversity gain, but there are not designed to achieve an additional coding gain. Hence, OSTBCs must be concatenated with an external code which allows a significant coding gain.FEC (forward error correction) is a technique used for detecting and possibly correcting errors that can occur when messages are transmitted through a digital communication system, also for rendering the information more reliable. Thus, with staffing these coding techniques that are able to reach Shannon limits, in MIMO systems, better performances can be achieved by taking advantages of diversity and coding gains. The objective of this paper is to compare different FEC codes in Rayleigh fading channel and propose an appropriate code for MIMO-OSTBC systems. The simulation results reveal the performance of the proposed model
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