Particle Swarm Optimization with Directed Mutation

In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.

Improved particle swarm optimization algorithm for fuzzy multi-class SVM

An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side

Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.

Immunity clone algorithm with particle swarm evolution

Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects. Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.

Particle Swarm Optimization with Adaptive Mutation

A new adaptive mutation particle swarm optimizer,which is based on the variance of the population's fitness,is presented in this paper.During the running time,the mutation probability for the current best particle is determined by two factors:the variance of the population's fitness and the current optimal solution.The ability of particle swarm optimization(PSO)algorithm to break away from the local optimum is greatly improved by the mutation.The experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO,but can also avoid the premature convergence problem effectively.

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization(PSO)is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation.However,PSO still has certain deficiencies,such as a poor trade-off between exploration and exploitation and premature convergence.Hence,this paper proposes a dual-stage hybrid learning particle swarm optimization(DHLPSO).In the algorithm,the iterative process is partitioned into two stages.The learning strategy used at each stage emphasizes exploration and exploitation,respectively.In the first stage,to increase population variety,a Manhattan distance based learning strategy is proposed.In this strategy,each particle chooses the furthest Manhattan distance particle and a better particle for learning.In the second stage,an excellent example learning strategy is adopted to perform local optimization operations on the population,in which each particle learns from the global optimal particle and a better particle.Utilizing the Gaussian mutation strategy,the algorithm’s searchability in particular multimodal functions is significantly enhanced.On benchmark functions from CEC 2013,DHLPSO is evaluated alongside other PSO variants already in existence.The comparison results clearly demonstrate that,compared to other cutting-edge PSO variations,DHLPSO implements highly competitive performance in handling global optimization problems.

基于自适应变异粒子群算法的风光储微网调度

为克服传统粒子群算法在求解时容易形成局部最优,求解精度低的不足,提出了一种基于自适应变异粒子群优化的微电网调度求解方法。惯性权重采用自适应正态分布递减,随着迭代次数的增加更新粒子位置的移动策略,并且在算法后期引入变异环节。为验证算法的有效性,文章与其他算法进行收敛性能对比,并对两种典型天气情况下的微网运行成本模型仿真求解,得到最优调度。算例结果表明,改进算法能够对粒子全局最优搜索优化,效果优于其他算法,可合理调配分布式电源出力时段,具有良好的可行性。

基于自适应变异粒子群的风光储微网调度

为克服传统粒子群算法(particle swarm optimization,PSO)在求解时容易形成局部最优,求解精度低的不足,提出了一种基于自适应变异粒子群优化(adaptive mutation particle swarm optimization,AMPSO)的微电网调度求解方法。AMPSO惯性权重采用自适应正态分布递减,随着迭代次数的增加更新粒子位置的移动策略,并且在算法后期引入变异环节。为验证算法的有效性,该算法与其他改进算法进行收敛性能对比,并对4种典型天气情况下的微网运行成本模型仿真求解,得到最优调度。算例仿真结果表明,AMPSO能够对粒子全局最优搜索优化,在解决微网经济性运行问题上效果优于其他算法,可合理调配各微电源出力时段,具有良好的灵活性和可行性。

分布式光伏配电网电压无功优化研究

为解决分布式光伏接入配电网引起的电压越限质量问题,建立以有功网损和电压偏差最小为目标的无功优化数学模型。通过对光伏并网点的电压进行分析,提出了一种加权方式的电压功率与静止无功发生器控制补偿相结合的协同控制策略。为提高模型的求解能力,采用改进的粒子群优化算法,引入变异操作防止算法陷入局部最优;为提高算法的收敛效果,采用改进的异步学习因子。在IEEE-33节点配电系统中进行算例验证,结果表明了模型的正确性和策略的有效性。

复杂多方向威胁下的导弹预警雷达优化部署方法

针对现有导弹预警雷达部署相对独立、协同困难,难以满足大规模对抗场景的现状,从远程预警雷达、跟踪识别雷达、机动式预警雷达不同的任务特点出发,建立应对复杂多方向威胁的多型导弹预警雷达优化部署模型,在满足最优覆盖、协同交接、目标识别等任务约束下,解决雷达协同部署问题。针对所提模型设计了一种基于云自适应的分区优化离散粒子群(CPBPSO)算法,通过设计分区编码策略缩减算法求解空间、加入云自适应变异算子提高算法全局寻优和局部跳出能力,使算法更适用于导弹预警雷达部署问题的处理。实例验证了所提模型在求解单方向、多方向威胁场景部署问题的可行性,对比分析了CPBPSO算法的有效性,基本满足导弹预警雷达最优化协同部署的需求。

基于改进T分布烟花-粒子群算法的AUV全局路径规划

针对传统粒子群算法在处理自主水下机器人(Autonomous Underwater Vehicle,AUV)全局路径规划时面临的寻优时间长、能耗高的问题,本文提出一种改进的T分布烟花-粒子群算法(T-distribution Fireworks-Particle Swarm Optimization Algorithm,TFWA-PSO),该算法融合了烟花算法的高效全局搜索能力和粒子群算法的快速局部寻优特性.在变异阶段,提出自适应T分布变异来扩大搜索范围,并在理论上证明了该变异方式能够使个体在局部最优解附近增强搜索能力.在选择阶段提出了适应度选择策略,淘汰适应度差的个体,解决了传统烟花算法易丢失优秀个体的问题,并对改进的T分布烟花算法与传统烟花算法的收敛速度进行对比.将改进算法的爆炸操作、变异操作和选择策略融合到粒子群算法中,对粒子群算法的速度更新公式进行了改进,同时从理论上对所改进的算法进行了收敛性证明.仿真实验结果表明,TFWA-PSO能够有效规划出一条最短路径,同时与给定的智能优化算法相比,TFWA-PSO在寻找最优路径的时间上平均降低了24.72%,能耗平均降低了17.33%,路径长度平均降低了16.96%.

基于多策略混合改进MVO算法的光伏多峰MPPT研究

光伏阵列的电功率输出在局部遮荫工况下具有多峰特性,且随外界环境的变化而变化.为实现高效电能输出,利用多元宇宙优化(MVO)算法在解决低维度、小规模优化问题中的突出优势进行最大功率点跟踪(MPPT),并融合多种策略对其存在的缺陷进行改进.采用拉丁超立方抽样策略初始化宇宙种群,并对按照轮盘赌策略随机交换的宇宙实施柯西变异,提高宇宙种群的多样性.同时引入莱维飞行式量子粒子群优化(QPSO)算法,且对虫洞存在概率及旅行距离率进行自适应调整,以增强算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他算法,采用该算法的MPPT时间减少了45%以上,精度亦有所提高,从而具有更优的MPPT性能,可有效提高光伏发电效率.

基于自适应变异粒子群算法的装配式建筑施工安全投入优化

针对装配式建筑施工安全投入问题,以实际工程项目为例,对装配式建筑施工风险因素进行了二级分解;基于数学规划理论建立了非线性规划模型,并引入3种函数关系进行拟合;采用自适应变异粒子群算法在Matlab中进行求解,得到了相对较优方案。结果表明:在安全风险主要因素中,物的风险因素对安全投入最为敏感;在二级风险因素中,预制构件吊装器具的选择最为敏感,该结论可为日后施工安全投入的方向提供参考依据。

基于CMPSO算法的无人机复杂三维路径规划

为了增强粒子群优化(PSO)算法跳出局部最优的能力,以及提高PSO算法在解决复杂实际工程问题的寻优性能,提升PSO算法在路径规划问题中的优化效果,提出了一种融合压缩策略和变异策略的粒子群(CMPSO)算法。在压缩策略基础上,设计了全新变异策略,二者的融合有效实现了粒子的自适应调节、增强了种群的多样性,提高了算法跳出局部最优的概率。经过CEC2017部分测试函数的验证,证明了CMPSO算法优秀的寻优能力。最后综合考虑多种约束条件,将CMPSO算法应用于无人机复杂路径规划,与其他改进PSO算法比较,CMPSO算法能够获得更短的路径航程,且耗时更少,代价更小。表明该算法能为无人机复杂路径规划提供有力技术支撑。

基于数据驱动的分布式低碳能源站状态预测方法

分布式低碳能源站(distributed low-carbon energy station,DLCES)能提高能源利用效率和可再生能源消纳率,准确预测DLCES的未来运行状态能保障其安全可靠运行。为此,提出一种基于数据驱动的分布式低碳能源站状态预测方法。首先,分析DLCES结构与运行状态,利用关键状态量和偏移量变化将运行状态划分为正常、恢复、临界及紧急状态;其次,构建深度长短期记忆(long-short term memory,LSTM)模型,并利用改进粒子群算法进行超参数优化,提升预测模型性能;最后,利用测试集数据对柯西变异的粒子群算法(Cauchy mutation particle swarm optimization,CMPSO)和LSTM相结合的模型进行预测仿真,将其与RNN、LSTM及BP神经网络预测结果对比分析。结果表明:CMPSO-LSTM模型能提高预测效果,更具实际意义。

基于降维粒子群的大电网断面功率极限计算方法

随着电力系统的发展,系统运行方式日益复杂。为了提高电力系统运行安全水平,提出一种基于降维粒子群的大电网断面功率极限计算方法。首先综合考虑大电网静态安全稳定约束和暂态稳定约束,建立断面功率极限计算模型;然后,提出了基于功率灵敏度的降维粒子群算法,根据功率灵敏度和聚合系数对可调节发电机组进行分群处理,利用降维粒子群算法计算得到了极限功率、发电机组功率调节量以及极限功率制约因素,实现了大电网断面功率极限的快速寻优。最后在我国某地区2128节点电网和我国某跨区12643节点联网中验证了该方法的有效性和正确性。

面向动态公交的离散分层记忆粒子群优化算法

随着智慧城市、智慧交通的发展,移动互联网和公交智能基础设施以及相关数据的不断完善,通过用户手机预约公交服务的新型公交运营方式——动态公交,已经成为许多城市公交发展的重要探索方向。但目前,对动态公交问题的建模、算法研究不足。基于这一研究现状,提出动态公交问题模型和面向动态公交的离散分层记忆粒子群优化(PSO)算法。首先给出动态公交问题的目标函数和约束条件,给出动态公交问题的解的形式,并定义解的编辑距离;其次提出使用数据驱动的预计算路径集生成PSO算法的优质初始解的方法,给出基于解的编辑距离的PSO算法中粒子的变异概率和自适应收敛系数的计算方式;最后提出将粒子群分层求解的方法,其中低层粒子群可复用、可继承,从而减少单时间片内、时间片间复制和重初始化带来的性能损耗。基于重庆市北碚区蔡家岗街道的真实场景和亿级历史数据建立仿真环境进行实验,实验结果表明:相对于不分层PSO算法,分层PSO算法通过复用和继承能缩短超80%计算用时;自适应参数和变异机制能帮助算法更稳定地收敛到更优解;相对于传统公交系统,动态公交能在同等运力限制下,提高22%的乘客接单率,节省39.1%的乘客出行时间,所提算法能满足公交运营商在片区内进行动态公交调度的需求;相对于对比算法,所提算法平均缩短了85.3%的计算用时,并且在仅耗用80%里程的情况下提高了至少12%的接单率。

基于AMCPSO优化Kriging插值的温度补偿方法研究

为了降低温度变化对转换力传感器测量精度的影响,提出一种自适应变异混沌粒子群算法(AMCPSO)优化Kriging插值的温度补偿算法(AMCPSO-Kriging)。研发转换力传感器,分析温度对传感器输出的影响,建立温度补偿标定实验平台,通过标定实验获得建立温度补偿模型所需要的样本集,采用数据稀疏化方法对样本数据进行优化。通过Kriging插值构建了温度补偿模型,利用AMCPSO算法以交叉验证方式下模型预测产生的均方根误差和作为适应度函数,对Kriging插值中的范围参数θ和平滑度参数pk进行寻优求解,得到性能最佳的温度补偿模型。基于AMCPSO-Kriging温度补偿模型对转换力传感器的测量效果进行实验验证,与标准力传感器进行对比。实验结果表明:对样本数据进行稀疏化处理,算法平均运行时间从1076 s减少到6 s,提高了温度补偿算法的运行效率。在−20~70℃温度范围内,经过AMCPSO算法优化的Kriging模型有效提高了转换力传感器的测量精度,相比于未经AMCPSO算法优化的Kriging插值,转换力传感器测量的平均满量程误差从1.2%FS降低到0.6%FS。通过现场实验验证温度补偿的效果,转换力传感器测量的绝对误差在70 N以内,最大满量程误差为2.3%FS。所提出的温度补偿方法有效消除了温度对传感器测量精度的影响,满足铁路工况使用要求,对转换力传感器在铁路上实际运用具有重要价值。