A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.

Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.

Spider monkey optimization based resource allocation and scheduling in fog computing environment

Spider monkey optimization(SMO)is a quite popular and recent swarm intelligence algorithm for numerical optimization.SMO is Fission-Fusion social structure based algorithm inspired by spider monkey’s behavior.The algorithm proves to be very efficient in solving various constrained and unconstrained optimization problems.This paper presents the application of SMO in fog computing.We propose a heuristic initialization based spider monkey optimization algorithm for resource allocation and scheduling in a fog computing network.The algorithm minimizes the total cost(service time and monetary cost)of tasks by choosing the optimal fog nodes.Longest job fastest processor(LJFP),shortest job fastest processor(SJFP),and minimum completion time(MCT)based initialization of SMO are proposed and compared with each other.The performance is compared based on the parameters of average cost,average service time,average monetary cost,and the average cost per schedule.The results demonstrate the efficacy of MCT-SMO as compared to other heuristic initialization based SMO algorithms and Particle Swarm Optimization(PSO).

Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic management and effective utilization of network resources are the key factors of LTE models.Moreover,there are some major issues in LTE that are to be considered are effective load scheduling and traffic management.Through LTE is a depraved technology,it is been suffering from these issues.On addressing that,this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing(SMO-ELB).In this model,load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm.The simulation results show that the proposed model provides better results than exiting works in terms of efficiency,packet delivery ratio,Call Dropping Ratio(CDR)and Call Blocking Ratio(CBR).

Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended user.With the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain.The existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia content.Since energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network lifetime.With this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for WMSN.The proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum computing.The proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance measures.The simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.