改进的群搜索优化算法在MATLAB中的实现

文章介绍了一种改进的带趋势预测的群搜索优化（Group Search Optimizer,GSO）算法,描述了此算法在MATLAB中的程序实现。同时根据函数优化的概念,举例说明了改进算法在函数优化中的应用方法。     

一种结合自适应局部搜索的粒子群优化算法

本文提出一种结合自适应局部搜索的混合粒子群优化算法.该方法在粒子群优化算法的全局搜索过程中,使用能根据当前种群搜索状态自适应地调整局部搜索空间大小的局部搜索算法加强其局部搜索能力.采用了著名的基准函数对算法的性能进行测试,并与其他已有算法进行了比较.结果表明,这种混合粒子群优化算法能获得更高的搜索成功率和质量更好的解,特别在高维复杂函数优化上具有很强的竞争力.     

改进共享策略的简单群搜索优化算法

本文对GSO算法进行了改进。采用了三种改进措施,一是使用新的信息共享策略,不仅共享最优个体,而且共享其它优秀个体的位置信息;二是采用完全随机搜索,不再按搜索角度进行搜索;三是限制游荡者扰动的维数。其中第一项改进是本文提出的。经过6个经典测试函数测试对比后可知,无论在高维还是低维情况下,改进算法的搜索性能都优于原GSO算法以及GA、PSO算法,在高维问题中尤其明显。     

一种基于单纯形搜索的粒子群优化算法

为了改善粒子群优化算法的求解性能,提出了一种基于单纯形搜索和粒子群优化的混合算法。该算法一方面自适应地确定惯性权重、认知以及社会参数来达到免参数目的,另一方面利用单纯形搜索来引导部分粒子的搜索方向,从而加速算法收敛。数值实验结果表明,与传统的粒子群算法和其他基于单纯形的粒子群算法相比,提出算法在评估次数、求解精度方面表现良好。     

一种小规模多种群萤火虫群优化算法

针对基本萤火虫群优化算法在求解多极值函数问题时,随着极值点增多,收敛速度低、精度不高的缺陷,提出了一种小规模多种群的改进萤火虫群算法,实验仿真表明,改进后的萤火虫群算法在求解多极值函数优化问题时,所花时间明显减少且精度也得到了提高。     

改进的群搜索优化算法

群搜索优化(Group Search Optimizer,GSO)算法是一种新的群集智能优化算法,适宜于解决多模态高维问题。对GSO算法进行了一些改进,简化了计算过程,提高了优化性能。主要在两个方面进行改进,一是在迭代过程中,控制允许变异的维的数量,使之从多到少变化,以提高收敛速度。二是用随机数来确定生成个体新位置所用的一组随机值的正负数比例,避免正负数比例趋于固定,增加随机性。经过6个常用测试函数测试及与其他文献结果对比后可知,在低维情况下,此算法与GA、EP、ES、PSO、GSO算法相比有较好的整体收敛性能,高维时,此算法与GA、PSO、GSO比较,收敛性能有明显优势。     

一种改进的多群协作粒子群优化算法

提出了一种改进的多群协作粒子群优化算法,该算法整个种群采用主从模式,分为一个主群和多个从群,多个从群粒子统一地进行初始化操作,从而避免了多个粒子群重复搜索现象。同时,算法采取了一种扰动策略,即当前全局最优解在扰动因子的迭代周期内保持不变时,就重置粒子的速度,迫使粒子群摆脱局部极小。该算法不仅增加了种群的多样性,扩大了搜索范围,而且还改善整个种群易陷入局部极小值的缺陷。通过9个基准函数进行测试,实验结果表明,IMCPSO与MCPSO算法相比具有明显的优越性。     

一种嵌入局部混沌搜索的混合微粒群优化算法

该文研究了基于种群演化的微粒群优化算法,针对此算法在迭代的过程中陷入局部极小点而产生群体演化停滞的现象,提出了一种嵌入局部混沌搜索的混合微粒群优化算法。此混合方法利用混沌迭代的遍历性来增强算法的局部精确搜索能力从而达到全局搜索性能和局部搜索性能的平衡,使群体快速脱离停滞状态。实验结果表明,相比于其他演化搜索算法如标准微粒群算法,标准遗传算法和改进微粒群算法,嵌入局部混沌搜索的混合微粒群算法在收敛性和鲁棒性方面得到了较大的改善,很大程度上避免了演化停滞现象的发生,是一种高效的搜索方法。     

一种混合搜索的粒子群算法

本文通过对粒子群算法个体极值、全局极值和种群极值的结合,提出一种混合搜索粒子群算法.用典型的非线性测试函数进行仿真,其实验数据和收敛曲线验证了该算法的有效性,具有快速收敛效果和寻优能力.     

动态格点搜索的改进算法用于团簇结构快速优化

结合高效的动态格点搜索(DLS)算法与扰动操作(Perturbation Operation)提出一种新的改进方法(DLS-PO),用于确定团簇的最低能量结构。针对一个特定构型,DLS算法总能给出其对应搜索空间的最规则结构。然而,一次失败的DLS优化将消耗大量的运算资源。为此,采取原子移动和结构旋转的扰动操作成功地改变了构型,再结合后续的DLS操作,提高了优化效率。将该算法用于原子数高达309的Lennard-Jones团簇及100原子NP-B函数铝团簇的结构优化。优化结果显示相比于DLS算法,DLS-PO算法更为高效。     

一种改进的鲁棒多目标优化方法

针对在解决某些复杂多目标优化问题过程中,所得到的Pareto最优解易受设计参数或环境参数扰动的影响,引入了鲁棒的概念并提出一种改进的鲁棒多目标优化方法,它利用了经典的基于适应度函数期望和方差方法各自的优势,有效地将两种方法结合在一起。为了实现该方法,给出一种基于粒子群优化算法的多目标优化算法。仿真实例结果表明,所给出的方法能够得到更为鲁棒的Pareto最优解。     

An improved adaptive string searching algorithm

Sunday's OM algorithm can reduce the number of character comparisons by making use of information of character distribution in an alphabet. Smith's adaptive algorithm uses dynamic statistics to reduce comparisons and its performance is close to that of the OM algorithm in the number of character comparisons. Smith's algorithm has the advantage of language independence. Its drawback is that it runs slowly because of maintaining an ordering list. This paper presents an improved adaptive method which dispenses with the ordering list. This method treats the pattern as a circle, and first compares the mismatched character in the last checking operation. This methods is slightly worse than Smith's method in the number of character comparisons, but it much better in the running time. © 1998 John Wiley & Sons, Ltd.     

An improved brain storm optimization with chunking-grouping method

Brain storm optimization (BSO) is a population-based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according to the fitness and recombined to groups, is developed to balance each group with same quality-level. Afterwards, a new mutation strategy is designed in CIBSO and a learning mechanism is used to adaptively select appropriate strategy. Experiments on the CEC2014 test suite indicate that CIBSO is better or at least competitive performance against the compared BSO variants.     

基于改进群搜索优化算法的群体路径规划方法

针对群体动画中传统路径规划算法搜索时间长、寻优能力差等问题,提出一种利用群搜索算法进行多线程路径规划的方法。该方法首先将模拟退火算法引入到搜索模式中,克服算法易陷入局部最优的问题;其次,通过结合多线程和路径随机拼接技术,将算法应用到路径规划中。仿真实验表明该算法无论在高维还是低维情况下都具有较好的全局收敛性,能够很好地满足在复杂动画环境下路径规划的要求。     

一种改进的射线跟踪路径搜索算法

基于几何光学原理的射线跟踪信道建模方法能够对场强、时延、到达角等多径信息进行精确预测,该方法多用多边形或三角构建三维场景模型。为了提高射线跟踪的路径搜索效率,提出一种改进的三角化射线跟踪路径搜索算法。在原带符号体积法中加入对实际不相交三角形的排除过程,简化判断过程,减小计算量。以三个不同类型的场景模型为例,对改进前后的算法进行仿真比较。结果表明,改进的路径搜索算法比原算法运算速度更快,且场景模型越复杂,效率提高越明显。     

一种Grover量子搜索算法的改进策略

在使用Grover量子搜索算法对给定规模的数据库搜索时，随着搜索目标数的增加，获得正确结果的概率大幅度下降．分析了出现这种现象的原因，提出了一种基于新的相位匹配条件的改进策略．在新的相位匹配条件中，使2次相位旋转的大小相等方向相反．当要搜索的目标数目多于记录总数的1／3时，应用改进后的算法只需一步搜索，能以至少25／27的概率得到全部搜索目标．实验证明这种策略是有效的．     

An improved infeasible SSLE method for constrained optimization without strict complementarity

In this paper, using “working set” technique for determining the active set, a new SSLE-Type algorithm with arbitrary initial point for constrained optimization is presented. At each iteration, we first introduce a new working set based on a multiplier function, then an improved direction is obtained by three systems of linear equations with the same coefficient matrix and possess small scale, and to avoid the Maratos effect, another correction direction is yielded by a simple explicit formula. Under some mild conditions, the algorithm is proved to be globally and strongly convergent, and the superlinear or even quadratic convergence can be obtained without the strict complementarity. Finally, some interesting numerical results are reported.     

An improved critical constraint method for structural optimization of product families

This paper discusses important improvements in the efficient Critical Constraint Method (CCM) for the optimization of structural product families subjected to multiple crash load cases. The method was first presented by Öman and Nilsson (Struct Multidisc Optim 41(5):797–815, 2010). However, the algorithm often converged towards an infeasible solution, which considerably limited the applicability of the method. Therefore, improvements are presented here to make the method more robust regarding feasible solutions, resulting in only a minor decrease in efficiency compared to the original method. The improvements include; a penalty approach to control the feasibility of the method by continuously pushing the solution out of the infeasible region, a dynamic contraction algorithm to increase the accuracy and robustness of the method by considering the optimization progress and variable history in the reduction of the step size, and the implementation of a parallel approach to further increase the efficiency of the method by enabling the full potential of large-scale computer clusters. Finally, the potential of the improved CCM algorithm is demonstrated on a large-scale industrial family optimization problem and it is concluded that the high efficiency of the method enables the usage of large product family optimization in the design process.