求解全局优化问题的改进蝙蝠算法[英文]

蝙蝠算法(BA)是一类基于试探技巧的群智能优化算法,该算法已被广泛用于诸多领域问题的求解.本文提出一个改进的蝙蝠算法NIBA.在算法中,为了加强蝙蝠算法的局部和全局搜索能力,提出了三个改进策略.首先,为了改进蝙蝠的局部搜索能力,在当前最优解处给出了一个新的搜索方程.其次,为了改进算法的全局搜索能力,平衡算法的开发能力和探索能力,算法吸收并改进了和声搜索机制.最后,为了进一步提高NIBA算法的搜索能力,在当前最优解处,算法采用了混沌搜索机制.为了验证算法的性能,针对18个标准测试函数进行了数值实验.与其它算法的比较结果显示,NIBA算法具有更好的稳定性,且效率更高.     

一种新型的蝙蝠算法

针对基本的蝙蝠算法在搜索后期易陷入局部最优及寻优性能差等缺陷.提出一种新型的蝙蝠算法(IBA),重新定义了蝙蝠算法的速度迭代公式,将函数适应度值引入速度迭代公式中丰富了种群的多样性,提高了算法的全局及局部搜索性能,通过10个经典的函数及3个非线性方程组的测试,仿真结果表明,改进后的算法提高了解的精度和解的数量.并与其他算法相比,IBA算法具有一定的优越.     

求解一般整数规划问题的改进蝙蝠算法

蝙蝠算法是一种新型的智能优化算法,本文针对基本蝙蝠算法易陷入局部最优、过早处于停滞阶段等不足之处,在蝙蝠速度更新公式中引入了惯性权重,并采用权值动态递减的方式变换权重,更好地平衡了算法的全局搜索能力和局部搜索能力.通过求解一系列经典整数规划问题,并与已有算法进行比较,结果表明:改进的蝙蝠算法在一般整数规划问题的求解中具有较高的计算效率和精度,以及较强的全局搜索能力.     

基于速度越界处理与高斯扰动的改进蝙蝠算法

蝙蝠算法(Bat algorithm,BA)是一种新型的、搜索全局最优解的元启发式算法.为解决蝙蝠算法局部搜索时易陷入局部极值的问题,提出一种基于速度越界处理与高斯扰动的改进蝙蝠算法(VGBA).该算法利用速度的越界处理控制蝙蝠位置更新的范围,利用高斯扰动增强蝙蝠算法的全局搜索能力.选取8个测试问题进行数值实验,实验结果表明,VGBA算法在收敛精度和稳定性上比BA算法有显著提升.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

求解柔性作业车间调度问题的混合分布估计算法

针对柔性作业车间调度问题,提出了一种有效的混合分布估计算法.算法采用基于排序的编码和解码方法.为了保持种群多样性,采用k-均值聚类方法对种群进行分簇,从各子簇中选取具有代表性的若干个体组成优势种群以建立描述问题解空间分布的概率模型,该优势种群包含了全局统计信息及个体特征信息,利用变邻域搜技术优化种群中的最佳个体,避免其陷入局部最优.最后,通过算例仿真,表明算法具有良好的全局搜索能力和局部求精能力.     

非线性0-1规划问题的元胞灰狼优化算法

针对非线性0-1规划求解问题,基于元胞自动机原理和改进的灰狼算法,提出一种元胞灰狼优化算法.首先,为了避免基本灰狼算法种群分布的随机性问题,利用佳点集理论对灰狼种群进行初始化,增强算法种群的多样性,提高算法的全局收敛速度;其次,针对基本灰狼算法的开发和探索能力平衡能力差的问题,利用自适应精英学习策略分别对算法中的参数α、灰狼与猎物的距离进行修正,实现灰狼算法的全局搜索速度和开发探索能力的最优均衡性;最后,将元胞自动机的演化规则与次优解β灰狼位置以及第三优解δ灰狼位置进行更新,利用元胞及其邻居增强搜索过程的多样性和分布性,实现灰狼算法的全局优化能力;并选用14个典型的非线性0-1规划问题算例进行仿真解算,并将解算结果与其它算法进行比较,结果表明:该算法对大规模复杂问题求解的平均运行时间少10%左右,且具有较快的收敛速度、较多的最优解集和较好的全局寻优能力.     

基于改进蝙蝠算法的贝叶斯网络结构学习

针对蝙蝠算法在搜索评分阶段易陷入局部最优且收敛精度低,以及基于蝙蝠算法的贝叶斯网络结构学习不完善等缺点,将模拟退火算法的思想引入到蝙蝠算法中,并对某些蝙蝠个体进行高斯扰动,提出了一种改进蝙蝠算法的贝叶斯网络结构混合学习算法.混合算法首先应用最大最小父子节点集合算法(Max-min parents and children,MMPC)来构建初始无向网络的框架,然后利用改进的蝙蝠算法进行评分搜索并确定边的方向.最后把应用本算法学习的ALARM网,和蚁群算法(MMACO)、蜂群算法(MMABC)进行比较,结果表明本混合算法具有较强的学习能力和更好的收敛速度,并且能够得到与真实网络更匹配的贝叶斯网络.     

多点收缩混沌优化方法及全局收敛性证明

针对目前混沌优化算法在选取局部搜索空间时的盲目性,提出一种具有自适应调节局部搜索空间能力的多点收缩混沌优化方法.该方法在当前搜索空间搜索时保留多个较好搜索点,之后利用这些点来确定之后的局部搜索空间,以达到对不同的函数和当前搜索空间内已进行搜索次数的自适应效果.给出了该算法以概率1收敛的证明.仿真结果表明该算法有效的提高了混沌优化算法的性能,改善了混沌算法的实用性.     

求解不等式约束优化的可拓遗传算法

提出了求解不等式约束优化问题的可拓遗传算法.分别考虑种群中的可行解和不可行解,建立可拓关联函数对不可行解的优劣程度进行可拓评价,然后采用精英选择策略,确保每次迭代中均有一定数量和质量的不可行解被选择,从而避免种群陷入局部最优.引入了高斯变异维持种群多样性,提高算法搜索速度.通过对两个测试问题的实验和分析,验证了可拓遗传算法的可行性和有效性.     

一种基于自适应混合遗传算法的非线性函数优化方法

遗传算法因其具有的特性,它采用交换、复制和突变等方法,获取的解为全局最优解,而且无需计算函数的导数,是一种只考虑输入与输出关系的黑箱问题,适用于处理各种复杂问题.此文基于最优保存的思想,把最速下降法与最优保存和自适应遗传算法相结合,用于求解非线性函数优化问题,提出一种基于自适应混合遗传算法的非线性函数全局优化方法.     

Chaos-enhanced synchronized bat optimizer

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.     

A Comparison of Global Optimization Methods for the Design of a High-speed Civil Transport

The conceptual design of aircraft often entails a large number of nonlinear constraints that result in a nonconvex feasible design space and multiple local optima. The design of the high-speed civil transport (HSCT) is used as an example of a highly complex conceptual design with 26 design variables and 68 constraints. This paper compares three global optimization techniques on the HSCT problem and two test problems containing thousands of local optima and noise: multistart local optimizations using either sequential quadratic programming (SQP) as implemented in the design optimization tools (DOT) program or Snyman's dynamic search method, and a modified form of Jones' DIRECT global optimization algorithm. SQP is a local optimizer, while Snyman's algorithm is capable of moving through shallow local minima. The modified DIRECT algorithm is a global search method based on Lipschitzian optimization that locates small promising regions of design space and then uses a local optimizer to converge to the optimum. DOT and the dynamic search algorithms proved to be superior for finding a single optimum masked by noise of trigonometric form. The modified DIRECT algorithm was found to be better for locating the global optimum of functions with many widely separated true local optima.     

An improved particle swarm optimizer for parametric optimization of flexible satellite controller

Parametric optimization of flexible satellite controller is an essential for almost all modern satellites. Particle swarm algorithm is a global optimization algorithm but it suffers from two major shortcomings, that of, premature convergence and low searching accuracy. To solve these problems, this paper proposes an improved particle swarm optimization (IPSO) which substitute “poorly-fitted-particles” with a cross operation. Based on decision possibility, the cross operation can interchange local optima between three particles. Thereafter the swarm is split in two halves, and random number (s) get generated by crossing the dimension of particle from both halves. This produces a new swarm. Now the new swarm and old swarm are mixed, and based on relative fitness a half of the particles are selected for the next generation. As a result of the cross operation, IPSO can easily jump out of local optima, has improved searching accuracy and accelerates the convergence speed. Some test functions with different dimensions are used to analyze the performance of IPSO algorithm. Simulation results show that the IPSO has more advantages than standard PSO and Genetic Algorithm PSO (GAPSO). In that it has a more stable performance and lower level of complexity. Thus the IPSO is applied for parametric optimization of flexible satellite control, for a satellite having solar wings and antennae. Simulation results shows that the IPSO can effectively get the best controller parameters vis-a-vis the other optimization methods.     

混沌遗传算法对BP神经网络的改进研究

BP神经网络算法是目前应用最广泛的一种神经网络算法,但有收敛速度慢和易陷入局部极小值等缺陷.本文利用混沌遗传算法(CGA)具有混沌运动遍历性、遗传算法反演性的特性来改进BP神经网络算法.该算法的基本思想是用混沌遗传算法对BP神经网络算法的初始权值和初始阈值进行优化.把混沌变量加入遗传算法中,提高遗传算法的全局搜索能力和收敛速度;用混沌遗传算法优化后得到的最优解作为BP神经网络算法的初始权值和阈值.通过实验观察,改进后的结果与普通的BP神经网络算法的结果相比,具有更高的准确率.     

Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique

Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped in a local optima when solving complex multimodal problems. To address this concerning issue, we propose a novel PSO called as CSPSO to improve the performance of PSO on complex multimodal problems in the paper. Specifically, a stochastic search technique is used to execute the exploration in PSO, so as to help the algorithm to jump out of the likely local optima. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. Moreover, numerical simulation and comparisons with some typical existing algorithms demonstrate the superiority of the proposed algorithm.     

Bayesian stopping rules for multistart global optimization methods

By far the most efficient methods for global optimization are based on starting a local optimization routine from an appropriate subset of uniformly distributed starting points. As the number of local optima is frequently unknown in advance, it is a crucial problem when to stop the sequence of sampling and searching. By viewing a set of observed minima as a sample from a generalized multinomial distribution whose cells correspond to the local optima of the objective function, we obtain the posterior distribution of the number of local optima and of the relative size of their regions of attraction. This information is used to construct sequential Bayesian stopping rules which find the optimal trade off between reliability and computational effort.     

A hybrid optimization technique coupling an evolutionary and a local search algorithm

Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case.     

改进的布谷鸟算法求解物流配送中心选址问题

针对基本布谷鸟算法求解物流配送中心选址问题时存在搜索精度低、易陷入局部最优值的缺陷,提出一种改进的布谷鸟算法.算法采用基于寄生巢适应度值排序的自适应方法改进基本布谷鸟算法的惯性权重,以平衡算法的全局开发能力和局部探索能力;利用NEH领域搜索以提高算法的搜索精度和收敛速度;引入停止阻止策略对全局最优寄生巢位置进行变异避免算法陷入局部最优值、增加种群的多样性.通过实验仿真表明,改进的布谷鸟算法在求解物流配送中心选址问题上要优与基本布谷鸟算法以及其它智群算法,是一种有效的算法.     

An accelerated differential evolution algorithm with new operators for multi-damage detection in plate-like structures

This paper proposes an Accelerated Differential Evolution (ADE) algorithm for damage localization and quantification in plate-like structures. In this study, the inverse damage detection problem is formulated as a nonlinear optimization problem. The objective function is established through the alterations of the structure flexibility matrix weighted with a penalty-function, used specifically to prevent the detection of false alarms. The ADE algorithm is designed via the introduction of three modifications in the standard differential evolution algorithm. Firstly, the initial population is created using knowledge we usually have about the damage scenario of a structure. Such initialization technique assists the algorithm to converge promptly. Secondly, in the mutation phase, a new difference vector, created based on the dispersion of individuals through the search space, is used to ensure the automatic balance between global and local searching abilities. Thirdly, a new exchange operator is designed and used to avoid the untimely convergence to local optima. Finite-element models of isotropic and laminated composite plates are considered as numerical examples to test the efficiency of the proposed approach. Numerical results validate the performance of the ADE method, in terms of both solution accuracy and computational cost and highlight its ability to locate and assess damage, even for large-scale problems and noise-contaminated data.