An adaptive penalty scheme to solve constrained structural optimization problems by a Craziness based Particle Swarm Optimization

The use of Evolutionary Algorithms (EAs) to solve optimization problems has been increasing. One of the most used techniques is Particle Swarm Optimization (PSO), which is considered robust, efficient and competitive in comparison with other bio-inspired algorithms. EAs were originally designed to solve unconstrained optimization problems. However, the most significant problems, particularly those from real world optimization, present constraints. It is not trivial to define a strategy to handle constraints and, in general, penalty functions containing parameters to be set by the user and it may affect the search considerably. This paper consists of a combination of the Craziness based Particle Swarm Optimization (CRPSO) with an adaptive penalty technique, called Adaptive Penalty Method (APM), to solve constrained optimization problems. A CRPSO is adopted here in order to avoid premature convergence using a new velocity expression and an operator called “craziness velocity”. APM and its variants were applied in other EAs, originally in a Genetic Algorithm, which demonstrated its robustness. APM deals with inequality and equality constraints, and it is free of parameters to be defined by the user. In order to assess the applicability and performance of the algorithm, several structural engineering optimization problems traditionally found in the literature are used in the computational experiments.     

基于AHP-TOPSIS的可持续包装设计最优方案的筛选

目的 针对基本灰狼算法在函数优化过程中精度低、收敛速度慢、局部搜索能力差等问题,提出一种基于收敛因子和权重动态变化的自适应灰狼优化算法。方法 为了平衡算法的全局和局部搜索能力,引入聚焦距离变化率来动态调整收敛因子;使用自适应权重因子来改变算法的位置更新公式,以提高算法的收敛速度和精度。结果 仿真实验结果表明,改进后的算法在收敛精度和速度上都有了显著的提升,并且克服了灰狼算法在处理多峰函数时易陷入局部最优的缺点;对于纸浆浓度控制系统,控制效果更加理想。结论 通过改进的灰狼算法对PID控制器参数进行整定,可以显著提高系统的控制精度和其他性能指标,能更好地满足实际应用的要求。     

基于改进量子粒子群算法的纸浆浓度控制系统

目的为了克服传统PID控制在具有大时滞性、非线性等特点的纸浆浓度控制系统中性能不足和参数调整困难等问题,研究参数在线调整的方法。方法在传统PID控制的基础上,结合量子粒子群仿生算法(QPSO),提出一种量子粒子群算法优化的传统PID控制器参数,并应用于纸浆浓度控制系统;同时对基本量子粒子群算法进行改进,引入交叉算子,并将该控制算法应用到纸浆浓度控制系统中,并与传统控制进行对比。结果与传统PID控制和基本量子粒子群优化的PID相比较,改进的优化算法能够得到更加令人满意的控制效果,具有系统超调量小、响应速度快、鲁棒性高等优良的性能。结论基于改进的量子粒子群优化算法的纸浆浓度控制系统可有效控制纸浆浓度,能够明显提高系统的控制精度等性能指标,更好地满足实际应用的要求。     

基于改进灰狼算法的啤酒灌装PID液位控制

目的 针对啤酒罐装液位控制存在的变负荷、多模态、PID参数整定难的问题,提出一种基于改进灰狼算法的PID参数整定方法,以提高啤酒生产的工作效率。方法 对灰狼算法进行改进,使用欧式距离变化率动态调整收敛因子,平衡算法的全局搜索能力;引入动态自适应权重因子,提高算法的优化速度和精度;与基本灰狼算法比较并用测试函数验证改进算法的性能。结果 仿真结果表明,改进后的灰狼算法在收敛速度和精度上提升效果显著;改进灰狼算法整定的PID参数的上升时间为1.9s,调节时间为5.12 s,超调量为3.78%。结论 与基本灰狼算法对比,改进灰狼算法对啤酒灌装液位PID参数进行整定,调节时间快,超调较小,可以更好地满足啤酒生产的控制要求。     

基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究

针对移动机器人路径规划中使用蚁群算法(ACO)易陷入局部最优和收敛速度慢的问题,提出了一种适用于机器人静态路径寻优的改进免疫遗传优化蚁群算法(IMGAC)。该算法可以根据实际情况自动调整变异概率和变异方式,以及自动调节个体免疫位的长度,将通过改进的变异算子和免疫算子嵌入蚁群算法来提高全局寻优能力与收敛速度。仿真及实验表明:相比于经典ACO算法以及最大最小蚂蚁系统,IMGAC算法收敛速度更快,全局寻优能力更强。利用该算法寻找移动机器人最优路径,提高了静态路径寻优的效果和效率。     

大渡河流域梯级水电站中长期优化调度研究

瀑布沟电站已经正式开始蓄水,采用先进算法对其进行优化调度研究非常必要。针对粒子群算法存在早熟收敛现象和后期振荡现象,给出一种动态改变惯性权的自适应粒子群算法。该算法原理简单,易编程实现,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为梯级水电站中长期优化调度问题提供了一种有效的解决办法。     

基于改进PSO算法的结构损伤检测

结构的损伤检测常转化为求解约束优化问题,针对粒子群算法容易出现早熟问题,增大算法后期的粒子位置的改变量,从而增加粒子位置的差异,因而能够增强其在求解约束优化问题时抵抗局部极小的能力。两层刚架单损伤和多损伤识别的数值结果和收敛曲线表明了改进后的粒子群算法优于传统的带惯性因子的粒子群算法。三层框架结构的4种损伤工况的试验研究进一步说明了该算法应用于结构损伤检测领域的有效性。     

基于改进超螺旋算法的永磁同步电动机控制

目的 为提高传统PID控制下永磁同步电动机在遇到负载突变、重载启动、频繁变速等外界条件变化时性能突变的问题，文中将超螺旋滑模引入电机控制中，以更好地应对外界条件的变化。方法 通过在原有的超螺旋滑模算法中，加入自适应比例项和积分项，使其获得更快的收敛速度和更强的鲁棒性。然后引入一种新的过饱和系数，来应对引入积分项所带来的超调问题。结果 在Matlab/SMULINK的仿真环境中，搭建所提方法的模型，并与传统的PID控制、超螺旋滑模控制以及比例项改进超螺旋滑模控制所搭建的模型做对比。结果表明，文中方法在遇到外界条件变化下，稳定性提高了33%～91%、快速性提高了27%～76%。结论 文中优化后的超螺旋滑模控制器有更高的鲁棒性和更快的快速收敛性，改善了系统的性能，能够有效应对负载突变、重载启动、频繁变速等问题，使应用文中所提方法的永磁同步电动机能够更加符合包装机的要求。     

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.     

Enhanced Heap-Based Optimizer Algorithm for Solving Team FormationProblem

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO’s optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.     

基于改进布谷鸟搜索算法的永磁同步电机参数辨识

提出一种基于改进布谷鸟搜索算法的永磁同步电机参数辨识方法。针对布谷鸟搜索算法的不足,采用基于云隶属度的模糊推理调整巢主鸟发现外来鸟蛋的概率;采用自适应变步长的方法调整Lévy飞行步长。改进后的算法通过增加种群之间的多样性以加快收敛速度,提高了局部和全局寻优能力。永磁同步电机多参数辨识结果表明,改进布谷鸟搜索算法能有效地辨识电机各参数,与未改进算法相比,验证了改进算法的有效性和优越性能。     

A hybrid optimization method of harmony search and opposition-based learning

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. Optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.     

Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization (PSO), Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), and Grey Wolf Optimization (GWO). The obtained results showed the superiority of the RHSO algorithm over the selected algorithms; also, the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests. Further, RHSO is very effective in solving real issues with constraints and new search space. It is worth mentioning that the RHSO algorithm has a few variables, and it can achieve better performance than the selected algorithms in many test functions.     

微粒群算法在自动控制系统设计中的应用

提出了将微粒群优化(Particle Swarm Optimization,PSO)算法与控制系统设计相结合的系统设计思路和方法。系统设计过程包括两个部分：首先基于历史输入输出数据,用微粒群算法建立系统的模型,然后基于得到的模型进行控制器的设计,并用微粒群算法进行控制器的参数优化整定。仿真试验结果表明,微粒群算法在控制系统设计的模型建立、控制器参数优化等方面发挥了重要的作用,简化了控制系统设计任务,提高了设计效率。     

基于多目标优化PID的纸浆浓度控制系统

目的 针对纸浆浓度PID控制系统在时滞性、稳定性、耦合性方面的不足,提出一种基于多目标优化的纸浆浓度PID控制方法.方法 对纸浆生产工艺进行分析,结合纸浆浓度PID控制系统,设定多属性的决策变量,建立对应的目标函数和约束条件;从质量、产量、成本、环境等4个方面对纸浆浓度PID控制过程进行多目标优化,构建基于多目标优化的纸浆浓度PID控制模型;采用改进量子粒子群算法对多目标优化模型进行求解,获得Pareto最优纸浆浓度控制方案;将建模方法、优化算法、优选方法进行耦合,从而形成"建模-求解-优选"全过程的纸浆浓度控制方法.结果 通过对纸浆浓度控制优化前后的决策变量进行比较分析可知,多目标优化PID控制方法在评价指标方面满足了质优、高产、低耗的多目标优化的可控性要求;相较于传统PID控制方法,IPSO-PID控制方法的响应速度更快,具有更好的鲁棒性;在PID参数优化方面,文中的优化模型整定控制参数在0.05 s内达到稳态阶段,稳态误差更低,具有更好的稳定性.结论 在保证系统鲁棒性的同时,基于多目标优化算法的纸浆浓度PID控制系统可实现对纸浆浓度的精确性和稳定性控制,更好地满足实际工业生产的要求,确保纸张质量的品质.     

基于PLC的颗粒包装机称量系统设计

目的 为提高颗粒包装机称量精度和稳定性,基于PLC设计一种颗粒包装机称量系统.方法 分析颗粒包装机结构和工艺流程,并给出控制系统结构,包括核心处理器PLC、交流控制器、伺服电机驱动器、传感器、三相电机和伺服电机等.以称量控制为主要研究对象,提出一种粒子群模糊PID称量控制器,以提高称量控制系统的收敛速度、通用性和可移植性.最后进行仿真和实验研究.结果 相关结果表明,与模糊PID控制器相比,加入粒子群优化算法后,系统的响应速度更快,达到稳定状态所需时间更短,实际包装误差仅为0.528％.结论 所述称量控制系统可以有效地提升称量精度,有利于提高颗粒包装机的自动化水平.     

动态定量称量包装系统BP神经网络PID控制算法

目的针对动态定量称量包装控制系统具有大惯性、滞后、非线性且无法建立精确数学模型等缺点,研究提高动态定量称量包装系统控制精度的方法。方法提出了一种改进型BP神经网络PID的定量称量包装控制系统,将BP神经网络与PID控制方法相结合,通过神经网络的自学习、加权系数的调整,优化PID控制器参数K_i,K_p,K_d,并将粒子群算法引入到神经网络中作为其学习算法,以有效提高BP神经网络算法的收敛速度。结果仿真和实验结果表明,改进型BP神经网络PID控制响应速度快、超调量较小,系统称量误差得到大幅度减小。结论所述控制方法可以明显提高定量称量控制过程的稳定性、精确性以及鲁棒性。     

基于PSO和LSSVM回归的摄像机标定

针对摄像机非线性显式标定时很难精确地建立其复杂的数学模型,本文提出了基于粒子群优化算法(PSO)和最小二乘支持向量机(LSSVM)回归的摄像机非线性隐式标定方法.该方法采用最小二乘回归机精确逼近图像坐标与世界坐标之间复杂的非线性成像关系;利用PSO算法搜索LSSVM回归模型的最优参数,提高LSSVM回归的收敛速度和泛化能力.通过运用标准BP神经网络、遗传算法、LSSVM及粒子群优化的LSSVM回归方法对圆阵列图案标定模板进行标定,实验结果表明:基于PSO和LSSVM回归的标定方法具有标定精度高、收敛速度快、泛化能力强等优点.     

求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

A novel parameter estimation method for metal oxide surge arrester models

Accurate modelling and exact determination of Metal Oxide (MO) surge arrester parameters are very important for arrester allocation, insulation coordination studies and systems reliability calculations. In this paper, a new technique, which is the combination of Adaptive Particle Swarm Optimization (APSO) and Ant Colony Optimization (ACO) algorithms and linking the MATLAB and EMTP, is proposed to estimate the parameters of MO surge arrester models. The proposed algorithm is named Modified Adaptive Particle Swarm Optimization (MAPSO). In the proposed algorithm, to overcome the drawback of the PSO algorithm (convergence to local optima), the inertia weight is tuned by using fuzzy rules and the cognitive and the social parameters are self-adaptively adjusted. Also, to improve the global search capability and prevent the convergence to local minima, ACO algorithm is combined to the proposed APSO algorithm. The transient models of MO surge arrester have been simulated by using ATP-EMTP. The results of simulations have been applied to the program, which is based on MAPSO algorithm and can determine the fitness and parameters of different models. The validity and the accuracy of estimated parameters of surge arrester models are assessed by comparing the predicted residual voltage with experimental results.