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.

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.

基于混合策略改进的鹈鹕优化算法

针对鹈鹕优化算法求解精度低、稳定性不足、易陷入局部最优等问题,文章提出一种混合策略改进的鹈鹕优化算法(IPOA)。首先,为了增强种群的随机性和多样性,扩大种群的搜索范围,引入反向折射学习机制;其次,利用正余弦算法和鹈鹕算法融合,改进鹈鹕搜索猎物的方式,增强算法的局部搜索与全局搜索能力;然后,采用Levy飞行机制对鹈鹕位置进行更新,从而提高算法的搜索能力以寻找最优值;最后,引入自适应t分布变异算子,使用算法的迭代次数作为t分布的自由度参数来增强鹈鹕种群的多样性,避免算法陷入局部最优。通过12个标准测试函数对改进算法与海鸥优化算法、黑猩猩优化算法、鲸鱼优化算法、蛇群优化算法和基本鹈鹕优化算法进行测试比较,结果表明,IPOA具有更好的收敛速度和稳定性。最后将改进鹈鹕算法应用于压力容器设计优化问题,进一步证实改进后的算法具有较好的求解性能。

混合策略改进的野马优化算法

针对野马优化算法存在种群多样性低、收敛速度慢和易陷入局部最优等问题,提出一种混合策略改进的野马优化算法(IWHO)。在马驹位置公式中引入基于饥饿游戏的Tent惯性权重,更好平衡算法的全局搜索与局部搜索能力;在放牧阶段引入折射镜像学习策略,利用折射镜像学习生成可行解的反向解,加快算法的求解速度;利用混合黄金正弦与飞蛾扑火算子,使算法跳出局部最优。将改进后的算法(IWHO)和其它算法在10个基准函数上对比测试,并通过Wilcoxon秩和检验和拉/压弹簧设计问题验证算法性能。仿真结果表明,IWHO在收敛速度和寻优精度上有明显改进。

基于折射反向学习的改进正弦余弦探路者算法

针对探路者算法(Pathfinder Algorithm,PFA)在寻优时收敛速度慢、求解精度低与极易陷入局部最优等问题,提出一种基于折射反向学习的改进正弦余弦探路者算法运用于函数优化问题当中。首先,通过折射反向学习策略初始化种群,利用折射与反向原理相结合使初始解更加靠近最优解位置,优质的种群定位能为迭代期的策略执行提供良好基础;其次,在探路者位置更新阶段引入改进的正弦余弦个体位置更新方式,该方式将原更新式中的线性步长搜索因子进行替换,以非规律的模式产生新代探路者个体,从而降低个体忽略最优解的概率,同时提出一种自适应权重添加至原更新式当中,配合正、余弦函数对算法的全局搜索与局部开发能力进行平衡;最后,将本文算法运用于12个经典的基准测试函数与10个具有复杂特征的CEC2014基准测试函数上进行寻优求解,并将其运用于压力容器设计与三杆桁设计问题,同时选取了合适的评价指标对算法性能进行评估。实验结果表明:本文算法在收敛速度、寻优精度与局部最优规避性方面均有较大提升,出色的工程优化性能也证明了本文算法的鲁棒性。

海洋捕食者算法的改进及其应用

针对传统的计算方法在求解多约束工程优化问题时存在的不足,提出一种融合多种策略的海洋捕食者算法并应用其中。算法首先采用折射反向学习策略,以增加初始种群的多样性。其次,引入正弦-余弦算法和高斯-柯西变异算子,用于提高求解精度和全局寻优能力。最后,通过6个标准测试函数和2个工程实际案例的应用,表明了新算法的优越性。

基于改进蝠鲼优化算法的光伏组件参数辨识模型

为了解决当前光伏组件模型中存在的参数辨识精度低和稳定性差的问题,提出了一种基于折射学习机制的蝠鲼觅食优化算法的三二极管光伏组件参数辨识模型(RLMRFO-TDM)。该模型将差分进化机制融入到MRFO算法的种群更新环节,提高了MRFO算法的局部探索能力,并加快了MRFO算法收敛速度;引入折射学习机制改善了MRFO算法的随机性,提高了种群在搜索区域中的离散性和MRFO算法的全局搜索能力。利用基准测试函数,验证了RLMRFO算法的有效性;采用STP6-120/36和STM6-40/36两种光伏组件的数据集对RLMRFO-TDM模型的参数辨识进行性能测试,与其他模型相比,RLMRFO-TDM模型的辨识精度、稳定性以及收敛速度表现最优。

Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.

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.

基于Halton序列改进蝠鲼算法的K-means图像分割

图像分割在日常生活中扮演着重要角色,传统的K-means图像分割具有随机性且容易陷入局部最优等缺陷,使得分割质量大大降低。为改善这些现象,提出一种基于Halton序列改进蝠鲼觅食优化(HMRFO)算法的K-means图像分割,HMRFO采用Halton序列初始化种群,使得个体位置充分均匀,再引入折射反向学习提升算法的全局搜索能力,最后引入新型的高斯变异策略,减小算法陷入局部最优的概率。在6个基准测试函数中对比了5种算法,验证了HMRFO的有效性及可行性。同时,将其应用于K-means图像分割中,与其他4种算法进行对比,结果显示HMRFO优化K-means具有较好的分割质量及泛化能力。

多策略融合的改进天鹰优化算法

为了解决天鹰优化算法(Aquila Optimization algorithm,AO)易陷入局部最优及收敛速度慢的问题,本文提出一种多策略融合的改进天鹰优化算法(Multi-Strategy Integration Aquila Optimization algorithm,MSIAO).该算法采用结合Tent混沌映射的折射反向学习初始化种群以提高算法前期的搜索效率,根据种内互助及优化策略解决算法寻优停滞的缺陷,并通过基于Bernoulli混沌序列的自适应权重策略提高算法的收敛速度,引入了柯西-高斯变异算子增强算法迭代后期逃逸局部极值的能力.本文对10个基准函数、部分CEC2014测试函数集进行实验,并将MSIAO用于2个工程设计优化问题.结果表明,对于高维单峰、高维多峰以及固定维复杂多模态函数,MSIAO比AO具有更高的收敛精度和更快的收敛速度;MSIAO对压力容器与焊接梁优化设计的经济成本较AO分别节约4.62%、0.77%,验证了MSIAO对于处理机械工程问题的实用性和优越性.

电离层探测及模化技术研究综述

电离层作为日地空间环境的重要组成部分,会对穿越其中的无线电波产生折射、反射、散射和吸收等效应,从而影响卫星导航、通信、雷达等诸多无线电信息系统的性能.利用各类技术手段来探测电离层的特征参量,揭示其内含的变化规律,并在此基础上构建出满足空间科学研究与系统应用的电离层模型,具有重要的价值.本文首先介绍了现有常用的地基和天基电离层探测手段;其次,调研总结了国内外在经验电离层模型、理论电离层模型、数据驱动电离层模型及机器学习建模方面的发展现状;再次,给出了导航系统电离层单频延迟修正、雷达系统电离层折射误差修正和通信/导航系统信号闪烁中断预警等三个典型应用场景中的电离层模型开发情况;最后,对电离层探测和模化技术的发展趋势进行了分析.

多策略离散人工蜂群算法设计FIR低通数字滤波器

针对人工蜂群(Artificial Bee Colony,ABC)算法在解决复杂问题表现出来精度不高以及收敛速度较慢的不足,提出了一种融合折射学习和Lévy飞行的多策略离散人工蜂群算法(Discrete Artificial Bee Colony Fusing Refraction Learning and Lévy flight,DABC-RL),用于设计有限长脉冲响应(Finite Impulse Response,FIR)低通数字滤波器,以期进一步提高其滤波性能。在DABC-RL算法中,一方面,Lévy飞行策略用于增强ABC算法的局部搜索能力,折射学习用于增强ABC算法的全局搜索能力;另一方面,通过设计合适的离散编码方案对DABC-RL算法中的候选解进行离散化,使其适合于设计FIR低通数字滤波器。为了测试所提的DABC-RL算法设计的FIR低通数字滤波器的性能,选取由ABC算法、基于折射学习的refrPSO算法所设计的FIR低通数字滤波器作为对比算法。实验结果表明,相比其他算法,DABC-RL算法所设计的FIR低通数字滤波器的性能最好,且获得了最快的收敛精度和收敛速度。

种群分段变异学习和S型权重变色龙群算法

探索寻优能力强、良好的可靠性和稳定性是智能算法应用到具体领域中的最佳选择。针对变色龙群算法存在求解不稳定、收敛精度低下和搜索开发之间不平衡等缺陷,提出一种种群多样性分段变异学习和S型权重的变色龙群算法(RMSCSA)。引入折射镜像学习(refraction mirror learning,RML)策略使变色龙更符合自然界中的观察,增强它的多样性;引入种群多样性分段变异使适应度较差的个体得到保留,并引导它向最优位置靠近;S型递减权重的引入让它进一步平衡算法的全局搜索和开发能力,通过收敛性分析得出S型递减权重因子的优势。利用经典函数集和CEC 2017函数集进行性能验证,结果表明3种策略比CSA具有更好寻优精度和效率。通过对独立运行30次的结果进行Wilcoxon秩和检验、Friedman’s以及Holm后续检验统计分析,结果表明引入的3种策略与CSA相比都有更好的寻优能力。

改进麻雀搜索算法优化SVM的异常点检测

支持向量机是检测异常点的常用方法,但其仍然存在难以高效获取最优参数,导致检测效率低、稳定性差的问题。鉴于此,提出一种改进的麻雀搜索算法ISSA,并将其用于优化支持向量机参数。首先,采用改进折射反向学习和可变对数螺线改进传统麻雀搜索算法;然后,利用改进麻雀搜索算法ISSA对支持向量机参数进行优化;最后,将优化后的支持向量机用于异常点检测。仿真实验结果表明,在G-mean和F-measure 2个评价指标上,利用ISSA优化后的支持向量机检测效果明显优于其它3种分类算法,具有更优秀的检测效率、稳定性和泛化能力。

深度学习屈光检测方法研究

屈光不正是一种非常常见且对视功能发育有严重危害的眼科问题。准确与方便的屈光检测技术,对于及时发现屈光不正问题以及采取相应措施进行干预具有非常重要的意义。目前的屈光筛查设备虽然能较快进行屈光检测,但主要存在两个问题:检测准确度较低,对被测者配合度要求较高。因此,提出一种新的屈光检测方法,此方法使用基于偏心摄影验光原理的光学系统获取人脸面部近红外图像,使用图像处理技术对面部近红外图像进行处理,得到左右瞳孔图像和瞳孔位置信息,使用提出的结合了深度可分离卷积和SE模块的混合数据多输入神经网络模型进行训练与屈光度的计算。与传统偏心摄影验光原理的屈光检测方法相比,此方法有望随着数据集的扩增而达到更高的准确度,并且此方法将瞳孔位置信息作为模型的输入,可以解决传统算法对被测者配合度要求较高的问题。该研究是对屈光检测新方法的一种有益探索,使用此方法有利于屈光筛查更便利地进行,为实现非接触自助式的屈光筛查提供基础。

Hybrid Modified Chimp Optimization Algorithm and Reinforcement Learning for Global Numeric Optimization

Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

基于改进被囊群算法的图像多阈值分割方法

为提升图像分割的准确度和性能,提出改进被囊群算法(improved tunicate swarm algorithm,IMTSA)的多阈值分割方法。基于折射反向学习初始化种群,采用精英反向学习重新选择优势个体,结合学生分布协调全局和局部搜索能力。在图像分割中,基于Otsu和最大熵方法,在不同阈值场景下,IMTSA与其它算法进行性能对比。实验结果表明,IMTSA在3类基准函数中均表现更好,图像分割效果与阈值数量成正比,其整体分割性能具有一定优势。