快速群搜索优化算法及其应用研究

在群搜索优化算法GSO(Group Search Optimize)基本原理的基础上,提出了改进的群搜索优化算法——快速群搜索优化算法QGSO(Quick Group Search Optimize),并应用于结构优化设计。算法的改进主要有3个方面:第一,当算法不前进时,适当加大游荡者的数目;第二,引进粒子群算法(PSO)的搜索方式,将GSO中的角度搜索改为步长搜索,并考虑群体最优值和个体最优值;第三,引入遗传算法,通过个体最优值与群体最优值的杂交重新生成游荡者。采用QGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与GSO优化算法和启发式粒子群优化算法(HPSO)的计算结果进行了比较,结果表明:该文改进的群搜索优化算法QGSO与GSO算法和HPSO算法相比具有较好的收敛精度和更快的收敛速度,可应用于工程结构的优化设计。     

用于结构优化设计的改进多目标群搜索算法

在多目标群搜索算法（multi-objective group search optimization, MGSO）基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法（IMGSO）,并应用于多目标的结构优化设计．算法的改进主要有3个方面：第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度．采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明：IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计．     

一种改进的人工蜂群算法及其在桁架几何优化设计中的应用研究

由于人工蜂群(artificial bee colony,ABC)算法存在收敛速度慢、易陷入局部最优的缺点,采用设置自适应缩放因子和基于适应度排序的选择方式代替传统的轮盘赌模型,提出了一种改进的快速人工蜂群算法(fast artificial bee colony,FABC).基于这种FABC算法对4个离散变量的几何优化模型进行了优化,并与遗传算法(GA)、蚁群算法(ACA)、启发式粒子群优化算法(HPSO)和群搜索算法(GSO)作了比较.结果表明,这种改进的人工蜂群算法具有较好的收敛精度.另外,ABC算法以及FABC算法结构简单,可应用在其他优化问题上.     

改进的细菌觅食优化算法及其在框架结构设计中的应用

在细菌觅食算法(bacterial foraging algorithm,BFA)基本原理的基础上,结合“和声搜索”算法,提出了一种改进的细菌觅食算法,并应用于框架结构优化.采用该优化算法分别对一单跨8层框架和双跨5层框架结构进行离散变量的质量优化设计,并与细菌觅食算法的计算结果进行了比较.计算结果表明改进的细菌觅食算法应用在结构优化中具有较好的收敛速度和精度,可应用于工程结构的优化设计.     

求解VRP问题的混沌模拟退火萤火虫算法

目的使萤火虫优化算法(GSO)能够适用于车辆路径问题(VRP)的求解,同时提高该算法的求解性能。方法通过对GSO算法的改进,提出求解VRP问题的混沌模拟退火萤火虫优化算法(CSAGSO)。首先,设计改进的GSO算法(IGSO)使IGSO算法能够适应VRP问题的求解;其次,在IGSO算法中引入模拟退火机制,提出模拟退火萤火虫优化算法(SAGSO),使IGSO算法可有效避免陷入局部极小并最终趋于全局最优。然后,在SAGSO算法中引入混沌机制,提出CSAGSO算法,对SAGSO算法的荧光素浓度值进行混沌初始化和混沌扰动;最后,对标准算例集进行仿真测试。结果与遗传算法、蚁群算法和粒子群算法相比,CSAGSO算法的全局寻优能力、收敛速度及稳定性均改善了50%以上。结论对GSO算法的改进是合理的,且CSAGSO算法的全局优化能力、收敛速度和稳定性均优于遗传算法、蚁群算法和粒子群算法。     

GEP在桁架结构优化中的应用

介绍基因表达式编程(GEP)算法的基本原理和在参数优化中的实现过程,并将该算法应用于桁架结构的优化设计。针对标准GEP容易陷入局部最优解,且收敛速度慢的缺陷,对算法引入回溯机制,用停滞前一代的精英个体替换当前种群中所有适应度最差的个体,使较优个体有更多机会向不同方向进化,扩大最优解的搜索空间。25杆空间桁架的截面优化设计结果证明:算法改进后,搜索效率得到明显提高,并通过72杆空间桁架算例,证明了该方法在结构优化中的可行性和有效性。     

粒子群优化算法在电网规划中的应用

粒子群算法适合求解连续变量优化问题,本文提出了粒子群算法的新离散化方法。常规粒子群算法在电力系统优化问题中取得了成功,但有"趋同性"。本文提出了改进多粒子群优化算法(IPPSO),IPPSO是两层结构:底层用多个粒子群相互独立地搜索解空间以扩大搜索范围;上层用1个粒子群追逐当前全局最优解以加快收敛。粒子群以及粒子状态更新策略不要求相同。     

基于随机权重粒子群和K-均值聚类的图像分割

K-均值聚类具有简单、快速的特点,因此被广泛应用于图像分割领域.但K-均值聚类容易陷入局部最优,影响图像分割效果.针对K-均值的缺点,提出一种基于随机权重粒子群优化（RWPSO）和K-均值聚类的图像分割算法RWPSOK.在算法运行初期,利用随机权重粒子群优化的全局搜索能力,避免算法陷入局部最优;在算法运行后期,利用K-均值聚类的局部搜索能力,实现算法快速收敛.实验表明：RWPSOK算法能有效地克服K-均值聚类易陷入局部最优的缺点,图像分割效果得到了明显改善;与传统粒子群与K-均值聚类混合算法（PSOK）相比,RWPSOK算法具有更好的分割效果和更高的分割效率.     

基于改进狮群算法的汽轮机热耗率模型预测

针对汽轮机热消耗率模型难以精准预测的问题,提出一种基于改进的狮群算法和快速学习网综合建模的方法。首先,针对传统狮群算法易早熟收敛以及在迭代后期寻优速度缓慢导致算法陷入局部最优的缺陷,通过引入禁忌搜索、非线性扰动因子以及黄金正弦策略进行改进;其次,对改进后的狮群算法进行数值验证,结果证明其具有更高的收敛精度和收敛速度;最后,采用某热电厂汽轮机的运行数据建立汽轮机热消耗率预测模型,并将改进狮群算法优化的快速学习网对其进行热耗率预测,将实验结果与其他优化策略进行对比验证,实验结果表明,基于改进狮群算法的快速学习网预测模型具有更高的泛化能力,提高了汽轮机热耗率的预测精度。     

多目标快速群搜索优化算法及在模型修正中的应用

为解决群搜索算法在求解多目标优化问题时易陷于局部最优或过早收敛,限制其在复杂结构模型修正中的应用问题,提出改进的群搜索优化算法-多目标快速群搜索优化算法(MQGSO)。采用LPS搜索方法对发现者进行迭代更新,能使发现者更快到达最优位置,提升寻优效率;对追随者增加速度更新机制,考虑其自身历史最优信息以保证收敛精度,并在算法后期采用交叉变异策略增加追随者个体多样性,避免陷入局部最优;在游荡者迭代更新中引入分量变异控制策略,增加其搜索的随机性,提高算法的全局寻优性能。通过7个典型多目标优化测试函数及某发射台有限元模型修正实例,对算法性能进行验证分析。结果表明,与已有MPSO(Multi-objective Particle Swarm Optimization)及MBFO(Multi-objective Bacterial Foraging Optimization)两种算法相比,所提MQGSO算法搜索性能更强、收敛速度更快、计算精度更高,不失为求解复杂多目标优化问题的有效方法。     

MULTIOBJECTIVE DESIGN OF A MARINE VEHICLE WITH AID OF DESIGN KNOWLEDGE

To generate the Pareto optimal set efficiently in multiobjective optimization, a hybrid optimizer is developed by coupling the genetic algorithm and the direct search method. This method determines a candidate region around the global optimum point by using the genetic algorithm, then searches the global optimum point by the direct search method concentrating in this region, thus reducing calculation time and increasing search efficiency. Although the hybrid optimizer provides cost-effectiveness, the design optimization process involves a number of tasks which require human expertise and experience. Therefore, methods of optimization and associated programs have been used mostly by experts in the real design world. Hence, this hybrid optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the objective function. Some domain knowledge is retrieved from database and design experts. The obtained knowledge is stored in the knowledge base. The results of this paper, through application to marine vehicle design with multiobjective optimization, show that the hybrid optimizer with aid of design knowledge can be a useful tool for multiobjective optimum design. © 1997 John Wiley & Sons, Ltd.     

Multiobjective design of actively controlled structures using a hybrid optimization method

A multiobjective approach to the combined structure and control optimization problem for flexible space structures is presented. The proposed formulation addresses robustness considerations for controller design, as well as a simultaneous determination of optimum actuator locations. The structural weight, controlled system energy, stability robustness index and damping augmentation provided by the active controller are considered as objective functions of the multiobjective problem which is solved using a cooperative game-theoretic approach. The actuator locations and the cross-sectional areas of structural members are treated as design variables. Since the actuator locations are spatially discrete, whereas the cross-sectional areas are continuous, the optimization problem has mixed discrete-continuous design variables. A solution approach to this problem based on a hybrid optimization scheme is presented. The hybrid optimizer is a synergetic blend of artificial genetic search and gradient-based search techniques. The computational procedure is demonstrated through the design of an ACOSS-FOUR space structure. The optimum solutions obtained using the hybrid optimizer are shown to outperform the optimum results obtained using gradient-based search techniques.     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm

In this paper, size and shape optimization of truss structures is performed using an efficient hybrid method. This algorithm uses a particle swarm strategy and ray optimizer, and utilizes additional harmony search for a better exploitation. Here, multiple frequency constraints are considered making the optimization a highly nonlinear problem. Some basic benchmark problems are solved by this hybrid method, and the numerical results demonstrate the efficiency and robustness of this method compared to other mathematical and heuristic algorithms.     

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.     

柔性板压电作动器的优化位置与主动控制实验研究

对柔性悬臂板主动控制中作动器的优化位置进行研究,其中作动器采用压电形式,优化算法采用粒子群方法,指标函数采用基于能量的可控Gramian优化配置准则。仿真和实验结果显示,粒子群优化算法能够有效地对作动器的优化位置进行计算,尤其适用于多个作动器的位置优化问题,基于作动器最优位置的控制设计能够取得良好的控制效果。     

基于动态改变权重粒子群算法的球度误差评定

为了实现对球形工件球度误差的精确评定,在4种球度误差评定数学模型的基础上,对文献提供的两组数据采用一种动态改变权重的粒子群算法(PSO)进行计算,这种算法在优化迭代过程中使惯性权重值随粒子的位置和目标函数的性质而更新。与基本PSO算法、最小二乘法、遗传算法和一种改进的PSO算法进行了比较。实验结果显示,相比其他方法,在最小包容区域法模型下使用动态改变权重粒子群算法得到的球度误差最小,第1组数据只需迭代30代左右,约50ms即可收敛,第2组数据收敛也很迅速,且多次实验显示其稳定性很高。因此,所提算法可精确快速地评价球度误差。     

Efficient procedures for the optimization of defects in photonic crystal structures

Seven different stochastic binary optimizers--based on the concepts of genetic algorithms and evolutionary strategies--are developed, applied to determine defect locations in several photonic crystal structures that serve as test cases, and compared by extensive statistical analysis. In addition to the stochastic optimizers, a quasi-deterministic optimizer based on an algorithm inspired by hill-climbing algorithms was implemented. The test cases include the prominent 90 degrees photonic crystal waveguide bend and a photonic crystal power divider. The analysis of the results shows that many different photonic crystal structures with high transmission may be found for any operating frequency. All of the eight optimizers outperform standard codes-because they maintain an incomplete fitness table-and find the global optima with a high probability even when the number of fitness evaluations is much smaller than the number of potential solutions contained in the discrete search space. Based on the incomplete fitness table, an algorithm to estimate bit-fitness values is presented. The bit-fitness values are then used to improve the performance of some algorithms. The four best algorithms-an extended microgenetic algorithm, two mutation-based algorithms, and the quasi-deterministic algorithm inspired by hill-climbing algorithms-are considered to be of high value for the optimization of defects in photonic crystals and for similar binary optimization problems.