An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

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.

基于反吸引速度更新机制的改进蜉蝣算法

针对蜉蝣算法(MA)前期收敛速度较慢、后期寻优精度不高等问题,提出一种基于反吸引速度更新机制的改进蜉蝣算法(MMOA)。采用改进型Tent混沌序列初始化蜉蝣种群,使蜉蝣分布更加均匀,提升了种群的多样性;结合MA的特点,引入反吸引速度更新机制指导蜉蝣速度更新,平衡算法的全局搜索和局部寻优能力,进而提升算法的收敛性能;对全局最优蜉蝣进行逐维的重心反向学习变异,降低各维度间的干扰,帮助算法跳出局部最优并加速收敛。基于12个标准测试函数和部分CEC2017测试函数进行对比仿真实验,结果表明:MMOA较灰狼优化(GWO)算法、MA等算法在收敛速度、寻优精度和稳定性等方面都具有明显优势。

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

基于熵值过滤和类质心优化的无监督域适应

无监督域适应作为机器学习领域的新兴研究方向之一,其主要利用源域监督信息来辅助无标记目标域的学习。截至目前,已有较多无监督域适应方法被提出,但在关系挖掘方面仍存在一些不足之处。具体来说,现有方法通常对目标域样本采取一致性处理策略,而忽略了目标域样本在关系挖掘中的差异性,因此文中提出了熵值过滤和类质心优化方法。所提方法利用生成对抗网络架构对目标域样本进行标记,利用所获伪标签计算样本熵值,并与所设阈值进行比较,从而进一步划分目标域样本。对于简单样本,分配伪标签;对于困难样本,该方法结合对比学习思想,利用源域和简单样本来学习更加鲁棒的分类器对困难样本分类,并进一步获得源和目标域的类质心。通过优化域间和实例对比对齐,来减小域间和域内的差异。最后,在3个标准数据集上与目前几种先进的领域自适应方法进行了对比实验,实验结果表明所提方法的性能均优于对比方法。

Centroid和EM结合的半监督文本分类

针对维吾尔文文本分类中的"标注瓶颈"问题,研究半监督文本分类。将期望最大化(expectation maximization,EM)算法和基于质心向量(Centroid vector)的分类算法相结合,提出一种半监督文本分类算法Centroid-EM,解决在Centroid分类器下,结合少量已标注样本和大量未标注样本来提高分类器性能的问题。在维吾尔文文本数据集上的实验结果表明,未标注样本的加入能够改善基于Centroid的分类方法在维吾尔文文本数据集上的分类效果。

融入重心反向学习和单纯形搜索的粒子群优化算法

针对粒子群优化PSO算法后期种群多样性差和易陷入局部最优解等问题,提出具备重心反向学习和单纯形搜索行为的粒子群优化COLS-PSO算法。初始时,基于混沌策略构造出搜索空间。进化过程中,基于Spearman系数选择需要进行重心反向学习的粒子,以帮助算法逃离局部极值区域。进一步引入局部搜索能力较强的单纯形搜索方法增强对最优粒子邻近区域的开发,以提高搜索精度。实验先在若干标准测试函数上进行,之后将COLS-PSO算法应用于软件测试数据生成问题。实验结果表明,COLS-PSO算法在求解精度、收敛速度和有效性方面表现较好,能够有效平衡种群多样性和算法收敛性的矛盾。

自适应变异蝴蝶优化算法

针对基本蝴蝶优化算法存在的收敛速度慢、求解精度低和易陷入局部最优等问题,提出一种自适应变异蝴蝶优化算法。首先,利用改进帐篷映射结合重心反向学习初始化种群,获得更好的初始解;其次,在位置更新处引入非线性惯性权重,平衡算法的全局搜索与局部搜索能力;最后,在算法运行过程中,根据群体适应度方差以及当前最优解大小来决定是否对当前最优解和最差解进行高斯变异二次寻优,增强算法跳出局部最优的能力。对12个基准测试函数的多种维度仿真实验结果表明,该算法在收敛速度、求解精度和寻优稳定性方面明显优于其他对比算法。

深度学习在脊柱质心定位与分割的应用进展

脊柱医学图像质心定位与椎骨分割在脊柱手术引导中具有重要意义,准确定位脊柱质心与分割椎骨已成为重要的研究课题。近年来,随着GPU算力的提高以及医学图像数据的累积,深度学习在脊柱图像中的应用取得突破。为研究深度学习在脊柱医学图像定位与分割任务中的应用现状与发展,文中对该领域近几年脊柱定位与分割模型进行整理与研究。收集脊柱常用数据集与评价指标并探讨深度学习模型在脊柱质心定位与分割的应用,分析模型实现过程及存在的不足之处。文中还针对目前深度学习在脊柱质心定位与分割应用面临的问题给出应对策略,并提出未来可行的发展方向。

融合学习机制的多混沌麻雀搜索算法

针对麻雀搜索算法(SSA)易受初始解的影响陷入局部极值、迭代后期收敛速度慢等缺陷,提出了一种融合学习机制的多混沌麻雀搜索算法(MMCSSA)。首先,引入重心反向学习策略(COBL)生成精英种群增强对多源优质搜索区域的勘探能力,提升算法的局部极值逃逸能力和收敛性能。其次,提出一种动态调整的黄金正弦领导策略并嵌入SSA中以改善发现者的搜索方式,增强算法的全局搜索能力。然后,提出一种基于学习机制的多混沌映射策略,该机制利用多混沌多扰动模式的特性,通过动态调用不同混沌映射赋予算法不同类别的扰动特征。混沌扰动失败时,引入高斯变异策略对当前解进行深度开发,两种策略协同作用,相互促进,极大增强了算法逃逸局部最优的能力。最后,将所提算法应用于12个不同特征的基准函数进行实验,结果表明与其他算法相比,MMCSSA在收敛精度、寻优速度和鲁棒性方面有更好的表现。

基于深度学习的遥感图像目标定位与计数

近年基于深度学习的语义/实例分割技术不断发展,分割性能持续提高,但对物体形状的精确分割仍然不轻松,并且很多任务中(即使不是遥感领域)可能对精确分割并无需求而只需要对目标进行位置估计。在遥感影像中定位物体质心是解决不需要对物体形状进行精确分割的一种简单方法。因此,本文对利用深度神经网络定位遥感图像目标质心的可行性进行了研究,对经典HRNet语义分割架构进行了修改和适应,提出了一种基于HRNet架构的专门用于目标定位和计数的全卷积神经网络Centroid-HRNet,在保持原模型结构的同时实现简单性的质心检测。使用“马萨诸塞州建筑数据集”对模型进行了测试和评估,与其他方法相比,本文评价结果达到了较好的精度,而且简单。

基于邻域重心反向学习的混合樽海鞘群蝴蝶优化算法

针对蝴蝶优化算法(BOA)收敛速度较慢和过早收敛到局部解的问题,提出一种基于邻域重心反向学习的混合樽海鞘群蝴蝶优化算法(HSSBOA)。首先,将樽海鞘群算法(SSA)引入BOA中,使算法快速处理局部搜索阶段,并更新种群位置,从而更有效地完成寻优过程,避免算法陷入局部最优;然后,引入邻域重心反向学习以便更好地帮助算法在邻域内进行小范围精确搜索,从而提高算法的精度;最后,引入动态切换概率以改善搜索中全局与局部的比重,从而加快算法的搜索速度。选取10个标准检测函数进行测试,将HSSBOA与几个先进的优化算法从收敛精度、高维度数据、收敛速度、Wilcoxon秩和检验和平均绝对误差(MAE)五个方面进行对比分析。研究结果表明,相较于其他算法,HSSBOA取得了更优的结果。消融实验进一步验证了各项改进均为正向作用。实例问题上的表现表明相较于其他方法,在求解有约束的复杂问题时,HSSBOA能够更有效地搜索出最优解。可见HSSBOA在寻优精度、稳定性和收敛效率等方面取得了一定的优势,并且能够求解复杂的现实问题。

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.

基于多分支注意网络与相似度学习策略的无监督行人重识别

无监督行人重识别的挑战在于学习没有真实标签的行人的判别性特征。为增强网络对行人特征的表达能力,进一步从空间和通道维度上提取更丰富的特征信息,提出了一种基于多分支注意网络的行人重识别特征提取方法。该方法通过捕获空间维度和通道维度上不同分支之间的交互信息,能够学习到更具判别性的行人特征表示。此外,针对噪声标签会对聚类质心产生干扰的问题,提出了相似度学习策略(SLS)。该策略先计算每个聚类中样本特征之间的相似性,然后选取相似性分数最高的特征向量所对应的样本进行对比学习,有效地缓解了聚类噪声导致的累积训练误差。实验结果表明,和无监督场景下的自步对比学习方法(SPCL)相比,在Market-1501,DukeMTMC-reID和MSMT17等3个数据集上的rank-1准确度分别提升了4.6%,3.3%和16.3%,显著地提高了无监督行人重识别的检索精度。

基于改进SSA-LSTM的销量预测研究

为提高销量预测的准确率,提出一种基于改进麻雀搜索算法优化LSTM的销量预测模型。首先在算法迭代过程中通过重心反向学习对个体进行变异,以增强算法跳出局部最优的能力;其次利用改进的算法对LSTM神经网络的超参数进行优化,解决了依靠主观经验选取超参数时存在精度不佳的问题;最后在原始销售数据的基础上加入大量零售数据等多个特征变量进行辅助预测,提高模型预测准确性。实验结果表明,该模型相较于其他模型具有更好的预测效果。

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.

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.