An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.

Levy Flight的发展和智能优化算法中的应用综述

Levy Flight源自纯数学概念,目前已被广泛应用于许多领域,如物理、生物、统计、金融和计算机科学等。目前,国内尚无文献对Levy Flight的发展及其在智能优化算法方面的应用进行总结。因此,文中首先回顾了Levy Flight的发展情况和应用,介绍了Levy Flight相关变体的基本原理和应用;然后着重讨论了近十年将Levy Flight应用于智能优化算法的研究,对其应用的方法进行了分类分析;最后总结了Levy Flight的未来发展趋势。文中的目的是让学者了解Levy flight的基本原理和其在智能优化算法中的发展情况,同时促进Levy Flight及其变体在众多学科尤其是计算机科学的发展和应用。

Array Antenna Pattern Synthesis Based on Selective Levy Flight Culture Wolf Pack Algorithm

Due to the shortcomings such as the premature convergence and the bad local optimal searching capability in traditional intelligence methods for pattern synthesis,a new type of wolf pack algorithm named Levy⁃Cultural Wolf Pack Algorithm(LCWPA)was designed on the basis of the Cultural Wolf Pack Algorithm(CWPA),which obeys the selective Levy flight.Because of the good overall management ability provided by the cultural algorithm in optimization process and the characteristics of excellent population diversity brought by Levy flight,the search efficiency of the new algorithm was greatly improved.When the algorithm was applied in the pattern synthesis of array antenna,the simulation results showed its high performance with multi⁃null and low side⁃lobe restrictions.In addition,the algorithm was superior to the Quantum Particle Swarm Optimization(QPSO),Particle Swarm Optimization(PSO),and Genetic Algorithm(GA)in optimization accuracy and operation speed,and is of very good generalization.

Species diversity in rock–paper–scissors game coupling with Levy flight

The rock–paper–scissors （RPS） game is a nice model to study the biodiversity in an ecosystem. However, in the previous studies only the nearest-neighbor interaction among the species was considered. In this paper, taking the long-range migration into account, the effects of the interplay between nearest-neighbor-interaction and long-range-interaction given by Levy flight with distance distribution lh （-3 ≤ h 〈-1） in the spatial RPS game are investigated. Taking the probability, exchange rate, and power-law exponent of Levy flight as parameters, the coexistence conditions of three species are given. The critical curves for stable coexistence of three species in the parameter space are presented. It is also found that Levy flight has interesting effects on the final spatiotemporal pattern of the system. The results reveal that the long-range-interaction given by Levy flight exhibits pronounced effects on biodiversity of the ecosystem.

Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

An artificial rabbit optimization(ARO)algorithm based on chaotic mapping and Levy flight improvement is proposed,which has the advantages of good initial population quality and fast convergence compared with the traditional ARO algorithm,called CLARO.CLARO is improved by applying three methods.Chaotic mapping is introduced,which can optimize the quality of the initial population of the algorithm.Add Levy flight in the exploration phase,which can avoid the algorithm from falling into a local optimum.The threshold of the energy factor is optimized,which can better balance exploration and exploitation.The efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics algorithms.At last,the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent,with Levy flight providing the most significant improvement in ARO performance.The experimental results show that CLARO has better results and faster convergence compared to other algorithms,while successfully addressing the drawbacks of ARO and being able to face more challenging problems.

基于改进粒子群优化的物流运输车路径规划方法

物流运输车路径规划问题是一个复杂的组合优化问题,因此,文章提出了一种基于改进粒子群优化算法的物流运输车路径规划方法,对粒子群优化算法中的惯性权值、学习因子和随机数进行了改进,并在算法的优化过程中引入了Levy flight模型,以避免过早的粒子群优化。并将该方法与蚁群算法和遗传算法进行了实验对比。实验结果表明,该方法能够有效降低了运输车的路径距离,显著提高物流运输的效率,降低了运输成本。

3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

一种自适应鲸鱼快速优化算法

针对标准鲸鱼优化算法存在的局部搜索能力不足、收敛速度慢等问题,提出了一种自适应鲸鱼快速优化算法AWOA。该算法根据个体的集散程度自适应选择全局搜索或局部搜索,在两者之间实现了动态平衡。针对偏离样本平均位置程度较高的个体引入Levy Flight进行二次优化,进一步扩大搜索区域,保证了算法的全局搜索能力。采用标准测试函数证实了AOWA具有较高的收敛速度及稳定性。将AWOA应用于无人车路径规划问题,仿真结果表明其具有稳定的局部搜索能力和全局搜索能力。

Multi-Topology Hierarchical Collaborative Hybrid Particle Swarm Optimization Algorithm for WSN

Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.

一种基于迭代自适应的鲸鱼优化算法

针对鲸鱼优化算法(WOA)后期收敛精度差和高维下容易陷入局部最优等不足,提出一种基于迭代自适应的鲸鱼优化算法(IAWOA)。首先用Circle混沌映射鲸鱼个体位置,使个体鲸鱼位置分布得更加均匀;其次随着迭代自适应改变螺旋气泡网大小,使螺旋气泡网前期较大而后期慢慢变小;再者用levy flight更新个体随机位置,帮助算法跳出局部最优;最后,通过12个基准测试函数,将IAWOA与其他3种算法进行仿真实验。实验结果表明,IAWOA有更好的收敛精度和鲁棒性。

Multi-Strategy-Driven Salp Swarm Algorithm for Global Optimization

In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum. Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm. Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources. The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA. In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems. The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

Multi-objective microgrid optimal dispatching based on improved bird swarm algorithm

Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization performance.Six typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its excellence.Finally,a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization.

An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5.While on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

Powder Mixing Simulation Using Random Walk Model in Eco-material Preparation

The eco-material composition is not well-distributed in preparation. The eco-material samples were taken for computer image analysis, and its particle numbers and appearance parameters were measured. Based on the mechanism of connective mixing and diffusion, the particles distribution was simulated by a computer using the random walk with Levy flight. The results show that the eco-material microstructure simulated by a computer has an idealized porous structure. The particles distribution has a cluster characteristic that changes with the different size and number of particles in Levy flight trajectory. Each cluster consists of a collection of clusters and shows a structure of self-similar cluster,hence presents a well-defined fractal property. The results obtained from SEM observation are in good agreement with the numerical simulations, and show that the convective mixing presents in the Levy flight walk.

An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network

The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.

Optimization of fracture reduction robot controller based on improved sparrow algorithm

The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is critical.To achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller parameters.An inverse kinematic analysis of the FRR was also performed.The robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control method.Both simulation and physical experiments were also performed.The actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through simulation.An optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized controller.The Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.

Improved deep mixed kernel randomized network for wind speed prediction

Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very reliable.Therefore,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed prediction.The proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation learning.This reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution time.Furthermore,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy.The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique.The lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.

mLBOA:A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization

Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.

An Improved Cuckoo Search Algorithm for Multi-Objective Optimization

The recently proposed Cuckoo search algorithm is an evolutionary algorithm based on probability. It surpasses other algorithms in solving the multi-modal discontinuous and nonlinear problems. Searches made by it are very efficient because it adopts Levy flight to carry out random walks. This paper proposes an improved version of cuckoo search for multi-objective problems（IMOCS）. Combined with nondominated sorting, crowding distance and Levy flights, elitism strategy is applied to improve the algorithm. Then numerical studies are conducted to compare the algorithm with DEMO and NSGA-II against some benchmark test functions. Result shows that our improved cuckoo search algorithm convergences rapidly and performs efficienly.