... four case investigations are conducted using MATLAB. ... MATLAB and Simulink files to program the drawing robot, mobile rover, and self-balancing motorcycle. He has been reviewer for leading journals (IEEE TSUSC, IEEE Access, MTAP-Springer, JOMS-Springer). ScienceDirect ® is a registered trademark of Elsevier B.V.Chaotic salp swarm algorithm for SDN multi-controller networks© 2018 Karabuk University. 21 Salp Swarm Algorithm: Modi cation and Application Essam H. Houssein, Ibrahim E. Mohamed , and Aboul Ella Hassanien 21.1 Introduction 21.2 Salp Swarm Algorithm (SSA) in brief 21.2.1 Inspiration Analysis 21.2.2 Mathematical Model for salp Chains 21.3 Modi cations of SSA Algorithm 21.3.1 Fuzzy Logic 21.3.2 Robust 21.3.3 Simplex Salp swarm algorithm (SSA) SSA is a meta-heuristic population based algorithm, which simulates the behavior of salps in oceans . The salp swarm algorithm (SSA) is a recent proposed meta-heuristic algorithm proposed by S. Mirjalili, et al. The SSA is stimulated by the swarming behavior of salp fishes that live in oceans, where they construct salp chain. Salps are a species of Salpidae that look like jelly fishes with transparent shape. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. By continuing you agree to the Copyright © 2020 Elsevier B.V. or its licensors or contributors. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The main inspiration of Salp Swarm Algorithm and Multi- ... model and pseudo code has been discussed in section 3. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this part, theoretical foundations of PSO are briefly reviewed. Salp swarm algorithm (SSA) is a population-based opti-mization method proposed by Mirjalili et al. MATLAB Central contributions by Seyedali Mirjalili. This paper proposes an enhancement to the salp swarm algorithm (ESSA).The ESSA is tested with twenty-three benchmark functions.The ESSA is compared with eight published algorithms.The ESSA and SSA are applied to the variable speed wind generators.This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. He also has research interest in ubiquitous and mobile computing, specifically in Internet of Things. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost.No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. By continuing you agree to the Copyright © 2020 Elsevier B.V. or its licensors or contributors. – Use this model (metamodel), and via an optimization algorithm obtained the values of the controllable variables (inputs/factors) that Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.His research interests are in distributed algorithms, communication, and cross-layered solutions to Wireless Sensor Networks. The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. BSSA is applied for feature selection. Publishing services by Elsevier B.V.ScienceDirect ® is a registered trademark of Elsevier B.V. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Professional Interests: Multi-objective optimization, Robust optimization, Swarm intelligence, Computational intelligence Swarm intelligence algorithms are a form of nature-based optimization algorithms.
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