基于改进粒子群算法的无人机航迹规划

针对当前基本粒子群算法无人机航迹规划在后期收敛速度比较慢、效率不高、易陷入局部最优等问题,提出一种改进粒子群算法。首先,在迭代前期和后期分段设置惯性权值的调整,实现粒子惯性和寻优行为的平衡;其次,设置一个定值与相邻2次适应度函数最优值比较策略,防止陷入局部最优;最后,引入遗传算法的交叉、变异机制,得出更优的结果。并通过仿真验证了改进粒子群算法在三维空间航迹规划的有效性和可行性。结果表明,与其他航迹规划算法相比,新算法具有路径长度更短、耗时更少、路径更平滑等优点,加快了收敛速度,提高了航迹规划效率和稳定性。因此,改进算法的航迹规划可得到满足约束关系的最优航迹,对实现自主飞行有重要的参考价值。     

混合邻域结构的粒子群算法

为了克服标准粒子群算法的早熟、停滞进化或易于陷入局部最优的现象,提出了一种混合模型(简称NSPO)。NSPO将一个粒子映射到无标度网络的多个网络节点上,借助网络结构获得该粒子的邻域拓扑。对粒子的更新,NSPO既考虑种群的最优,又考虑邻域的最优。在3个具有不同难度特点的测试函数上,将NSPO与标准粒子群算法进行了比较。实验结果表明:对于全局最优和梯度信息明显的函数,NSPO具有非常优越的表现;对于具有诸多局部最优的函数,NSPO逃逸局部最优的能力要强于标准粒子群算法;对于具有误导性梯度信息的函数,NSPO偶尔表现优异。     

基于改进粒子群的分布式通信干扰资源分配

为了解决分布式通信干扰场景下面临的资源分配效率低、干扰效益无保障等问题,结合通信干扰资源分配数学模型,设计了一种改进的粒子群算法。首先设计了分布式通信干扰场景并构建了通信干扰资源分配模型,以最大化干扰效益作为目标函数;其次采用自适应惯性因子和学习因子,并引入遗传变异策略和精英保留策略,提出一种改进的粒子群算法,最后对不同场景规模的通信干扰资源分配进行仿真实验。结果表明,相比小生境遗传算法、粒子群算法、遗传算法,改进的粒子群算法在不同场景规模下,均能获得更优的干扰效益,性能方面具备整体干扰效益更高、算法收敛速度更快、算法收敛误差更小等优势。所设计的改进粒子群算法可应用在分布式通信干扰场景中,为指挥决策提供参考。     

基于CPSO的基础设施模糊投资组合优化模型

针对基础设施效益模糊、难以度量的特点,结合模糊集理论,建立了模糊投资组合优化模型,改进粒子群算法,加入混沌思想,使用混沌粒子群算法(CPSO)求解基础设施的模糊投资组合优化模型。以4个城市投资公司的数据为样本,验证该方法的科学性与有效性。研究结果表明:模糊投资组合优化模型可较好地表征基础设施的模糊效益,提高基础设施投资决策的科学性;混沌寻优思想改进的粒子群算法可求得模糊投资组合优化模型的全局最优解,增强算法的鲁棒性。     

改进BP算法在旋转机械故障诊断中的应用

针对BP算法在神经网络诊断中表现出来的学习收敛速度慢、易于陷入局部最小点等局限性，用改进BP算法一有弹回的BP算法，对转子试验台模拟几种常见的故障进行诊断研究，从中得出结论：将代表故障的信息输入训练好的神经网络后，由输出的结果，便可以判断发生故障的类型。此外，有弹回的BP算法大大地提高了网络收敛速度和稳定性，更能满足实时在线诊断的要求。     

基于遗传微粒群算法的输电线路图像分割

图像分割技术的发展是电力系统视频监控技术发展的基础。最大类间方差(Otsu)阈值分割法分割图像计算复杂、时间开销大,需要利用遗传微粒群算法(PGSHEA)进行优化。PGSHEA引入微粒群算法的快收敛及遗传算法的多样性特点来优化参数搜索,同时引进了摒弃因子来节省时间开销,最终得到最优值。输电线图像实验结果表明,该算法不仅有助于提高识别的准确率,而且减少了时间开销。     

基于改进的蚁群算法的移动机器人路径规划

针对移动机器人路径规划中的传统蚁群算法收敛精度低、易陷入局部最优等问题,提出一种改进蚁群算法。首先,对算法的转移概率进行改进,加入转向代价,减少不必要的转折,并针对启发函数启发性能不够强,对路径启发信息进行改进;然后,提出一种自适应的参数调整伪随机状态转移策略,动态改变参数值,避免过早陷入搜索停滞,增强搜索的全面性,同时对信息素更新方式进行改进,调整信息素挥发系数,保持蚂蚁发现最优路径的能力;最后,通过Matlab与其他算法进行对比分析。仿真结果表明,改进的蚁群算法收敛速度快,且路径长度和算法迭代次数有明显减少,能得到全局最优路径。改进蚁群算法具有可行性、有效性,在移动机器人路径规划中有一定的应用价值。     

混沌果蝇-最小二乘支持向量机变压器DGA诊断

针对基本果蝇优化算法(FOA)寻优精度不高和易陷入局部最优的缺点,融合混沌算法对果蝇优化算法的进化机制进行改进,提出混沌果蝇优化算法(CFOA)。将CFOA算法应用于最小二乘支持向量机(LSSVM)惩罚因子和核函数参数的选择中,可以改善参数选择的随机性和盲目性,从而建立基于CFOA-ISSVM的故障模式预测模型。应用该模型对变压器油中溶解气体故障模式进行预测,结果表明,CFOA方法在在收敛速度、收敛可靠性及收敛精度上均比基本FOA有较大的提高,依此而建立的CFOA-LSSVM故障模式预测模型具有较高的准确率。     

两阶段遗传算法的结构及性能分析

针对基本遗传算法(简称BGA)常常存在局部收敛以及收敛解精度不高等方面的不足,提出了一种改进的算法——两阶段遗传算法,给出了算法的结构及具体的实施策略,进而利用Markov链理论和仿真技术分析了该算法的收敛性能,结果表明该算法具有操作简单、鲁棒性强等特点,不仅可以有效地避免寻优过程中的“早熟”现象,而且在很大程度上能提高最优解精度,适合于大规模、高精度的优化问题。     

流水工序调度与生产效率的关系模型分析

提出一种基于粒子群算法的流水工序调度任务优化模型。利用流水工序调度任务的特点得到流水工序时间约束条件,利用粒子群算法的原理建立流水工序调度任务优化模型,利用粒子群算法对模型进行求解。仿真实验表明,利用该算法能够得到流水工序调度问题的最优解,提高生产效率。     

Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942–1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

基于SVR-IPSO的高危行业企业安全投入优化模型及其改进——以煤炭生产企业为例

传统的安全投入模型对解决高危行业领域中模糊复杂的安全投入问题具有一定局限性,尤其当建立目标函数时,采用隐含线性关系假设的函数进行拟合会影响模型的推广能力。基于此,本文首先采用支持向量回归机（SVR）建立事故损失模型,与传统C-D函数拟合结果相比,该模型具有更好的预测能力;然后,以实际安全投入要求为约束,以安全总成本最小化为原则建立企业安全投入优化模型;最后,采用基于捕食搜索策略的粒子群算法对模型进行求解,同时,为保证全局收敛性,引入自适应控制策略对算法进行了改进。结果表明：该模型能够更加准确地描述安全投入与安全成本间的非线性作用关系,并通过粒子群寻优得到具备可行性的全局最优解,为高危行业企业安全投入结构优化提供新的决策思路。     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。