基于混沌搜索和权重学习的教与学优化算法及其应用

针对教与学优化算法容易陷入早熟收敛的问题,本研究提出了一种基于混沌搜索和权重学习的教与学优化（teaching-learning-based optimization algorithm based on chaotic search and weighted learning,TLBO-CSWL）算法。在TLBO-CSWL算法的教学阶段,不仅利用权重学习得到的个体来指引种群的进化,而且还使用正态分布随机数来替代原有的均匀随机数。另外,TLBO-CSWL还使用Logistics混沌搜索策略来提高其全局搜索能力。仿真结果表明,TLBO-CSWL的整体优化性能要好于其他所比较的算法。最后,将TLBO-CSWL用于求解非合作博弈纳什均衡问题,获得满意的结果。     

新型飞蛾火焰优化算法的研究

飞蛾火焰优化算法（Moth-Flame Optimization，MFO）是一种自然激励且易于实现的全局优化算法，在许多实际优化任务中表现出良好的性能。然而，MFO算法存在早熟收敛和容易陷入局部最优解的问题，针对这些不足，提出了一种Kent混沌动态惯性权值的改善飞蛾火焰优化算法（Ameliorative MFO，AMFO）。在AMFO算法中，引入Kent混沌映射搜索策略帮助当前最优解跳出局部最优；采用基于适应度值和迭代次数的动态惯性权值策略来平衡算法的开发和探索能力，以进一步提升MFO算法性能。在8个经典benchmark函数上验证AMFO算法的搜索精度和性能，并将其结果与标准飞蛾火焰优化算法、粒子群算法和差分进化算法进行比较，仿真结果表明AMFO算法具有较好的搜索性能。     

改进混沌方程及压缩感知理论图像加密算法

针对传统Logistic混沌系统混沌性能低,生成伪随机序列随机性较差等问题,本文提出一种新的改进Logistic混沌方程,并与Lorenz超混沌系统、压缩感知理论相结合构建一个多混沌图像压缩加密系统。在加密过程中与传统加密算法相结合,进行置乱、扩散操作最终获得密文图像。通过改进的Logistic混沌方程获得随机性能更好的伪随机序列来构造受控测量矩阵,仿真实验表明通过改进的Logistic混沌方程来构造的受控测量矩阵在压缩率为75%的条件下峰值信噪比达到34.26 dB,与传统Logistic混沌方程相比在同等条件下提高约10 dB,并且该算法有较好的抗差分攻击性能,像素改变率（NPCR）与统一平均变化程度（UACI）接近理论值。故本文提出的加密算法具有较好的压缩性、安全性以及信号重建特性。     

Hybrid parallel chaos optimization algorithm with harmony search algorithm

The application of chaotic sequences can be an interesting alternative to provide search diversity in an optimization procedure, named chaos optimization algorithm (COA). Since the chaotic motion is pseudo-randomness and chaotic sequences are sensitive to the initial conditions, the search ability of COA is usually effected by the starting values. Considering this weakness, parallel chaos optimization algorithm (PCOA) is studied in this paper. To obtain optimum solution accurately, harmony search algorithm (HSA) is integrated with PCOA to form a novel hybrid algorithm. Different chaotic maps are compared and the impacts of parallel parameter on the hybrid algorithm are discussed. Several simulation results are used to show the effective performance of the proposed hybrid algorithm.     

A novel reversible robust watermarking algorithm based on chaotic system

Reversible watermarking can be applied to the protection for important digital media, such as medical and military image, it allows the watermark to be extracted and the original image to be restored completely, but reversible watermarking with stronger robustness is seldom discussed in existing literature. In this paper, a novel reversible watermarking algorithm based on chaotic system is proposed; chaotic system is not only used to search space of reversibility of the scheme, but also used to randomly select the position of watermarking embedding. Consequently, the proposed scheme achieves larger threshold space of reversibility and better performance of security. For some specific thresholds, the proposed algorithm is not only reversible, but also has stronger robustness against image compression. The experimental results show that the ergodicity and sensitivity to initial values of chaotic system play an important role in reversible watermarking algorithm, and the proposed scheme can achieve balance between reversibility and robustness with the help of chaotic system.     

一种改进的混沌优化算法

为了克服遗传算法的早熟现象以及混沌优化的搜索时间过长的缺点,将遗传算法、混沌优化和变尺度方法相结合,提出了一种改进的混沌优化算法.该算法利用混沌的随机性、遍历性和规律性来避免陷入局部极小值,从而也克服了遗传算法中的早熟现象,同时引入了变尺度方法提高该算法的搜索速度.本文还给出了算法的收敛性分析.对典型测试函数的仿真结果表明此算法优于变尺度混沌优化和遗传算法.     

融合递减策略与Fuch混沌机制的改进YSGA算法

为增强绯鲵鲣算法搜索的覆盖性及寻优的精准性以优化全局探索能力和局部开采能力,提出一种融合步长因子递减策略与混沌局部增强机制的改进绯鲵鲣优化算法(IYSGA).首先,该改进算法在标准YSGA算法基础上,设计了一种动态的步长因子递变模式以实现绯鲵鲣算法高效全面的搜索,此策略有利于提高算法的搜索效率并扩大寻优范围;其次,混沌...     

A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems

The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.

     

Chaotic shuffled frog leaping algorithms for parameter identification of fractional-order chaotic systems

An accurate mathematical model has a vital role in controlling and synchronisation of chaotic dynamic systems. This paper proposes a shuffled frog leaping (SFL) algorithm and two chaotic versions of it to detect the unknown parameters and orders of chaotic models. The SFL by a grouping search strategy can provide a good exploration of search space. Also an independent local search for each group in this algorithm provides a proper exploitation ability. In the current research, to help the SFL to jump out of the likely local optima and to provide a better stochastic property to increase its convergence rate and resulting precision, the chaotic mapping is incorporated with the SFL. The superiority of the proposed algorithms is investigated on parameter identification of several typical fractional-order chaotic systems. Numerical simulation, comparisons with some typical existing algorithms and non-parametric analysis of obtained results show that the proposed methods have effective and robust performance. A considerably better performance of proposed algorithms based on average of objective functions demonstrates that the proposed idea can evolve robustness and consistence of SFL.     

耗散粒子群算法的性能分析

研究算法的计算性能问题,自组织耗散理论的引入,极大提高了粒子群算法性能,尤其改善了-粒子群算法搜索精度不高特别是对高维函数优化性能不佳的问题。但关于跳变因子的选择对算法性能影响显著。为保证结果精度和稳定性,通过大量的实验对耗散粒子群算法中的跳变因子对算法基本性能的影响进行了深入的分析,给出了跳变因子参数设置的指导原则。实验结果表明采用上述原则设计耗散粒子群算法既保证算法的运行效率又能有效地提高算法的全局搜索精度。     

一种采用改进CPSO算法的PID参数整定方法

为提高控制系统的性能,提出了一种采用改进混沌粒子群(CPSO)算法的PID参数整定方法。该算法将混沌搜索应用到粒子群算法的粒子位置和速度初始化、惯性权重优化、随机常数以及局部最优解邻域点的产生的全过程,使其不仅具有全局寻优能力,而且具有持续与精细的局部搜索能力。3种典型控制系统的PID参数整定实验结果验证了所提方法的有效性,其性能明显优于常规方法。     

用于特征选择的乌鸦搜索算法的研究与改进

特征选择是机器学习领域的热点问题.元启发式算法作为特征选择的重要方法之一,其性能会对问题求解产生直接影响.乌鸦搜索算法（CSA）是受乌鸦智能群体行为启发提出的一种元启发式算法,由于其具有简单、高效的特点,广大学者将其用来解决特征选择问题.然而,CSA易陷入局部最优解且收敛速度较慢,严重限制了算法求解能力.针对这一问题,采用logistic混沌映射、反向学习方法和差分进化这3种算子,结合乌鸦搜索算法,提出一种特征选择算法BICSA来选取最优特征子集.实验阶段,使用UCI数据库中的16个数据集来测试BICSA的性能.实验结果表明,与其他特征选择算法相比,BICSA求得的特征子集具有更高的分类准确率和较高的维度压缩能力,这说明BICSA在处理特征选择问题上具有很强的竞争力与足够的优越性.     

基数约束投资组合问题的一种混合元启发式算法求解

针对资产数目和投资资金比例受约束的投资组合选择这一NP难问题,基于混沌搜索、粒子群优化和引力搜索算法提出了一种新的混合元启发式搜索算法。该算法能很好地平衡开发能力和勘探能力,有效抑制了算法早熟收敛现象。标准测试函数的测试结果表明混合算法与标准的粒子群优化和引力搜索算法相比具有更好的寻优效率;实证分析进一步对混合算法与遗传算法及粒子群优化算法在求解这类投资组合选择问题的性能进行了比较。数值结果表明,混合算法在搜索具有高预期回报的非支配投资组合方面表现更好,取得了更为满意的结果。     

Chaotic cuckoo search

This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.     

鳍-水舱综合减摇混沌系统控制方法研究

针对船舶减摇问题,对综合减摇系统动力学模型方程进行分析,可知该系统为混沌系统。利用相图与Lyapunov指数谱分析方法,验证该系统在特定条件下的混沌行为,通过选取合理受控参数,利用非线性反馈控制方法使系统的混沌行为得到有效控制。该方法使系统混沌动力学行为得到了改善,并保留了系统原有的动力学特性。将混沌搜索算法与蚁群算法相结合,实现对PID控制参数寻优,使混沌蚁群算法不仅具备较强全局优化能力,与此同时,系统的收敛速度得到提高,该控制系统的性能得到增强。     

混沌梯度组合优化算法

提出一种混沌梯度组合全局优化算法，并对该算法进行了收敛性分析．算法首先采用改进的变步长梯度法得到某个优化值，然后利用变尺度混沌搜索跳出局部极小，经过反复组合迭代，直至到达最优解．仿真结果表明，该算法能充分发挥梯度法寻优的快速性和混沌法寻优的全局搜索能力．     

混沌梯度耦合优化算法及仿真研究

提出一种基于自适应混沌梯度下降的单目标耦合优化算法 .它采用变步长梯度下降法得到某个局部优化值 ,通过规则来判断其为局部极小值 ,然后利用一个由小到大变化的自适应尺度混沌遍历算法来获得一个更优值来代替局部极小值以跳出局部极小状态 ,全局优化值可以通过这种反复迭代来获得 .仿真结果表明 ,该算法能充分发挥梯度法寻优的快速性和混沌法寻优的全局搜索能力 ,有效地跳出局部极小 ,并快速找到最优值     

不同映射的混沌免疫进化算法性能分析

针对目前混沌免疫进化算法采用的混沌映射类型单一,并存在对混沌映射影响算法性能大小和机制缺乏深入研究等问题,分析和探讨基于不同混沌映射混沌免疫进化算法的性能。对几个典型测试函数的比较结果表明,Logistic-CIEA和Cubic-CIEA的性能易出现大波动,Kent-CIEA则具有相对稳定的收敛速度,表现出较强的鲁棒性。由此证明,混沌映射作为产生局部搜索轨迹的迭代函数,其混沌特性对算法性能影响较大。     

一种新型Skew Tent映射的混沌混合优化算法

针对已有的混沌优化算法几乎都是利用Logistic映射作为混沌序列发生器,而该混沌序列的概率密度函数呈两头多、中间少的切比雪夫型的分布性质,不利于搜索的效率和能力,为此,首先构造一种新型混沌映射序列发生器—Skew Tent映射并结合迭代优化特点加以改进,然后分析了它的混沌特性．其次,将改进的混沌映射与Alopex启发算法相结合,充分发挥Alopex算法的快速搜索能力和混沌优化全局寻优的特性,提出一种混沌混合优化算法,提高了算法的收敛速度和有效搜索全局最优解．最后,仿真算例验证了该算法的有效性和Skew Tent混沌映射的应用前景．     

融合多向学习的混沌麻雀搜索算法

对于原始麻雀搜索算法（SSA）在迭代过程中表现出的种群多样性减小,易陷入局部最优等问题,提出一种融合多向学习的混沌麻雀搜索算法（MSSA）。利用Hénon混沌映射初始化种群,增加麻雀种群的多样性,扩大可行解的搜索范围,为全局寻优奠定基础;采用多向学习策略增加麻雀跟随者探索未知领域的机会,平衡算法的局部开发性能和全局搜索能力;当算法陷入局部最优时,引用遗传算法中的变异策略依据动态的变异概率对当前最优个体进行扰动变异;将MSSA算法应用到无线传感器网络节点覆盖优化问题。数值实验结果与Wilcoxon秩和检验结果均表明MSSA算法在收敛精度与收敛速度等方面具有更明显的优势。