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Load Balancing in Software-Defined Networks Using spider monkey optimization algorithm for the Internet of Things

WIRELESS PERSONAL COMMUNICATIONS 2021年第1期116卷 23-43页

作者： Mayilsamy, Jayaprakash Rangasamy, Devi Priya AVS Engn Coll Dept CSE Mil Rd Salem 636003 Tamil Nadu India Kongu Engn Coll Dept Informat Technol Erode India

In the Internet of Things (IoT), the number of devices connected to the internet, and they can collect and exchange information at any time. IoT is helpful for the progress of a smart city and different applications. Software-Defined Network (SDN) offers programmability and flexibility in the IoT network. Nevertheless, the adoption of the number of gadgets will increase the transmission delay and this will lead the network to heavy loaded. To overcome this issue, an efficient load balancing technique has to be presented in the SDN network. By considering this solution as an aim, spider monkey optimization algorithm based load balancing (LB-SMOA) is presented in this paper. Using this technique, the controller with minimum load is selected and this selected controller balances the load of the heavily loaded controller. Simulation results show that the performance of the proposed LB-SMOA outperforms the existing load balancing techniques in terms of average response time, packet loss rate, and throughput.

关键词： Internet of Things (IoT) Software defined network (SDN) Load balancing spider monkey optimization algorithm SDN controller

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spider monkey optimization algorithm Based Collision-Free Navigation and Path optimization for a Mobile Robot in the Static Environment

Spider Monkey Optimization Algorithm Based Collision-Free Na...

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International Conference on Recent Innovations and Developments in Mechanical Engineering (ICRIDME)

作者： Lagaza, Kevin Pirewa Kashyap, Abhishek Kumar Pandey, Anish KIIT Univ Sch Mech Engn Campus 8 Bhubaneswar 751024 India

ISBN: (纸本)9789811501241;9789811501234

This paper concentrates on the recognition of a navigational algorithm, which can resolve path optimization challenges in minimum time. This navigational algorithm helps the mobile robot to avoid collision with obstacles. Implementation of spider monkey optimization (SMO) algorithm is carried out to explain the objectives of this paper. SMO algorithm is established upon the splitting-combination nature of spider monkeys. The proposed algorithm is examined in various terrains to determine the robustness and effectuality of the proposed algorithm. Simulation results confirmed that spider monkey optimization algorithm selects a path, which guides the robot to reach the target in minimum possible time. Subsequently, it decreases the computational cost, as the time for computation is minimized. In extension, we replicated the previously used environment, and the proposed algorithm is compared with the corresponding implemented algorithm. Our results demonstrate that spider monkey optimization (SMO) provides better results on the basis of time by selecting a path with lesser complexity. To determine the adequacy of the prescribed standard route-outlining control design, the common element of this paper is the numerical simulation.

关键词： Navigation Mobile robot spider monkey optimization algorithm Static obstacle Numerical simulation

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Model Predictive Control Based On spider monkey optimization algorithm of Interleaved Parallel Bidirectional DC-DC Converter 16

Model Predictive Control Based On Spider monkey optimization...

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16th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Lan, Yueyang Chen, Qihong Zhang, Liyan Long, Rong Wuhan Univ Technol Sch Automat Wuhan Peoples R China Huazhong Agr Univ Wuhan Peoples R China

ISBN: (纸本)9781728177090

Taking the high real-time performance, good stability and high reliability of DC-DC converter in electric vehicles as the research target, a constrained model predictive control (MPC) based on spider monkey optimization algorithm was proposed. Taking the Buck mode for an example, the equivalent circuit model is obtained under different switching states, and the state space model is established, then the prediction model is established through discretization. Define the appropriate cost function according to the control requirements and add the constraints of the control variables. The spider monkey optimization algorithm(SMO) is introduced to solve optimization problem of the constrained MPC in order to get the optimal solution more quickly and accurately. the simulation was carried out by MATLAB/Simulink, and the simulation results of PI control, MPC based on Particle Swarm optimization(PSO-MPC) and SMO-MPC was compared. The simulation show that the converter using MPC has better control effect, and SMO is feasible and effective.

关键词： DC-DC converters Model predictive control PI control spider monkey optimization algorithm

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Chaotic spider monkey optimization algorithm with Enhanced Learning 7th

Chaotic Spider Monkey Optimization Algorithm with Enhanced L...

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Sharma, Nirmala Kaur, Avinash Sharma, Harish Sharma, Ajay Bansal, Jagdish Chand Rajasthan Tech Univ Kota Rajasthan India Govt Engn Coll Jhalawar Rajasthan India South Asian Univ New Delhi India

ISBN: (纸本)9789811315923;9789811315916

spider monkey optimization (SMO) algorithm is a category of swarm intelligence-based algorithms, which mimics the fission-fusion social system (FFSS) comportment of spider monkeys. Although, SMO is proven to be a balanced algorithm, i.e., it balances the exploration and exploitation phenomena, sometimes the performance of SMO is degraded due to slow convergence in the search process. This article presents an efficient modified SMO algorithm which is capable of suppressing these inadequacies and is named as chaotic spider monkey optimization with enhanced learning (CSMO) algorithm. In this proposed algorithm, a chaotic factor is introduced in the Global Leader stage for providing appropriate stochastic nature and enhanced learning methods are commenced over the Local Leader stage and the Local Leader Learning stage in the form of learning method and exploring method, respectively. These changes help to enhance the exploration and exploitation proficiencies of SMO algorithm. Moreover, this proposed strategy is analysed on 12 different benchmark functions, and the results are being contrasted with original SMO and two of its recent variants, namely power law-based local search in SMO (PLSMO) and Levy flight SMO (LFSMO).

关键词： Meta-heuristic optimization techniques spider monkey optimization algorithm Swarm intelligence Chaotic factor

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Disruption Operator-based spider monkey optimization algorithm

Disruption Operator-based Spider Monkey Optimization Algorit...

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Kaur, Avinash Sharma, Harish Sharma, Nirmala Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509064717

spider monkey optimization (SMO) algorithm, is a neoteric Swarm Intelligence based algorithm, inducemented from the social comportment of spider monkeys, commonly known as fission-fusion social system. Despite of perpetuating an equilibrium state betwixt intensification and diversification by its own, SMO sometimes converges at a particular point due to its swarming nature. To overcome this problem, a new stage, namely disruption stage is incorporated with SMO. The proffered variant is named as Disruption operator-based spider monkey optimization (DiSMO) algorithm. In the incorporated stage, the disruption operator helps to scatter the swarms in convergence condition and according to the difference from the best solution, this operator is employed to entire solutions so that the solutions might attract or distract from the best solution. Further, the efficiency of the proffered strategy is estimated over 12 different benchmark functions and the outputs are being compared with basic SMO, its significant variant: power law based local search in SMO (PLSMO), and particle swarm optimization (PSO).

关键词： Meta-heuristic optimization Techniques spider monkey optimization algorithm Swarm Intelligence Disruption Operator

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Fitness Based Position Update in spider monkey optimization algorithm

Fitness Based Position Update in Spider Monkey Optimization ...

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International Conference on Soft Computing and Software Engineering (SCSE)

作者： Kumar, Sandeep Kumari, Rajani Sharma, Vivek Kumar Jagannath Univ Jaipur 303901 Rajasthan India

spider monkey optimization (SMO) technique is most recent member in the family of swarm optimization *** algorithm fall in class of Nature Inspired algorithm (NIA). SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： spider monkey optimization algorithm Swarm intelligence Engineering optimization problems Nature Inspired algorithms fission-fusion social structure

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Fitness Based Position Update in spider monkey optimization algorithm

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Procedia Computer Science 2015年 62卷 442-449页

作者： Sandeep Kumar Rajani Kumari Vivek Kumar Sharma Jagannath University Jaipur-303901 India

关键词： spider monkey optimization algorithm Swarm intelligence Engineering optimization problems Nature Inspired algorithms fission-fusion social structure.

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RETRACTED: Optimal rating and placing of numerous distributed generators in distribution network applying spider monkey optimization (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3251-3269页

作者： Sreedevi, S. Ezhilarasi, G. Angeline Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

The optimal location and rating of distributed generators in the distribution network is important role in the incremental consumer load. Proposed research determines a novel approach for the optimal placement rating of distributed generators. To achieve the multi-objectives of expanded work is to minimize the power loss, reduce operating cost, and development of magnitude output voltage. At present, the objectives are achieved by using spider monkey optimization algorithm (SMOA). Design and prescribed optimization is considered various constraints such as voltage limit, dg (Distributed Generator), real power generation limit, current limit, and power equality constraint estimated. The losses sensitivity factor involves the best selection of the optimal locations to fix dg units. In this context proposes to determine the optimal rating of dg units based on the searching behavior of spider monkeys. IEEE (Institute of Electrical and Electronics Engineering) 33-bus, and IEEE 69-bus distribution systems with various load levels are tested the validation of presented method. The entire work is done by MATLAB/Simulink program, and the SMOA is also compared with familiar algorithms to show the efficiency for an optimal rating of the power system.

关键词： spider monkey optimization algorithm power system distribution generator power quality simulation

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spider monkey optimization method to design two channel quadrature mirror filter bank with linear phase

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International Journal of Information Technology (Singapore) 2023年第1期15卷 499-510页

作者： Agrawal, Surendra Kumar Himani Electronics and Communication Engineering Department Government Women Engineering College Rajasthan Ajmer 305002 India MNIT Jaipur India

spider monkey optimization (SMO) algorithm is a newly introduced nature inspired algorithm (NIA) for solving complex optimization problems based on fission and fusion social structure (FFSS). In this work, two channel linear phase quadrature mirror filter (QMF) bank has been designed using SMO algorithm and a nearly ideal system response is achieved by optimizing the filter tap weights of prototype filter. The overall objective function of the two channel QMF bank which is to be minimized is a linear combination of three main parameters: pass-band error, stop-band residual energy of the prototype filter and square error of the transfer function at quadrature frequency. Simulations have been performed for low and high order QMF banks and the obtained results show that the performance of the suggested novel approach is superior than already existing nature inspired and gradient based algorithms. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Fission–fusion social structure Prototype filter Quadrature mirror filter bank spider monkey optimization algorithm

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Optimizing performance and economic viability of combined energy systems: A novel energy hub framework

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ALEXANDRIA ENGINEERING JOURNAL 2025年 124卷 188-203页

作者： Wang, Dan Ying, Chenlin Xian Jiaotong Univ City Coll Xian 710018 Shaanxi Peoples R China

This paper introduces a novel framework for optimizing combined energy systems through energy hubs, integrating electricity, heating, and cooling with renewable energy sources (RES) and demand response programs (DRPs). The framework addresses key uncertainties such as demand fluctuations for electricity, heating, and cooling, as well as variations in wind speed, solar radiation, and energy carrier prices. The proposed model is based on a mixed-integer nonlinear programming (MINLP) formulation, aiming to maximize energy hub profits by effectively utilizing DRPs and RES. To solve this complex optimization problem, the spider monkey optimization (SMO) algorithm is modified and applied. Simulation results demonstrate the model's effectiveness, showing significant improvements in energy hub profits, a reduction in grid power purchase costs, and a decrease in operational expenses. These findings highlight the novel contribution of the paper in enhancing the efficiency of energy hubs, improving flexibility, and supporting the integration of renewable resources for sustainable energy systems. The proposed optimization framework significantly enhances the economic efficiency of energy hubs by integrating renewable energy sources (RES) and demand response programs (DRPs). The results presented a clear trend of decreasing costs and electrical costs across the scenarios, with Scenario 1 having the highest values and Scenario 4 showing the most cost-effective outcomes. Specifically, the total cost decreases by approximately 11.83 % from Scenario 1 to Scenario 2, with further reductions in Scenario 3 and Scenario 4. Electrical costs follow a similar pattern, dropping by 33.14 % from Scenario 1 to Scenario 2 and continuing to decrease in the subsequent scenarios. The modified spider monkey optimization (SMO) algorithm outperforms PSO and GA, achieving a 3.2 % and 5.6 % higher objective function value, respectively, with a 25 % faster convergence rate.

关键词： Combined energy system Uncertainty modeling Demand response programs Renewable energy sources spider monkey optimization algorithm

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