Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

机组组合的混合编码遗传/tabu搜索组合算法

通过对遗传算法和tabu搜索的各自运算特性进行分析,文章提出了一种混合编码遗传算法与tabu搜索策略结合的组合算法,并运用组合算法对机组优化组合问题进行了求解。组合算法较好的结合了遗传算法的大规模寻优特性与tabu搜索的强局部搜索能力的特点,较大地减小了算法陷入局部最优的概率,能快速搜索到高质量的系统优化解;而且算法所采用的混合编码策略避免了每一迭代步上的负荷经济分配计算,大大地减少了计算量,提高了搜索速度。实例仿真结果表明,这种组合算法是有效的。

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing.Aiming at the job shop scheduling problem,a hybrid algorithm based on comprehensive search mechanisms(HACSM)is proposed to optimize the maximum completion time.HACSM combines three search methods with different optimization scales,including fireworks algorithm(FW),extended Akers graphical method(LS1+_AKERS_EXT),and tabu search algorithm(TS).FW realizes global search through information interaction and resource allocation,ensuring the diversity of the population.LS1+_AKERS_EXT realizes compound movement with Akers graphical method,so it has advanced global and local search capabilities.In LS1+_AKERS_EXT,the shortest path is the core of the algorithm,which directly affects the encoding and decoding of scheduling.In order to find the shortest path,an effective node expansion method is designed to improve the node expansion efficiency.In the part of centralized search,TS based on the neighborhood structure is used.Finally,the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature.

A multi-dimensional tabu search algorithm for the optimization of process planning

Computer-aided process planning （CAPP） is an essential component of computer integrated manufacturing （CIM） system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction （TAD） for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search （MDTS） algo-rithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence （OperSeq）, machine sequence （MacSeq）, tool sequence （TooISeq） and tool approach direction sequence （TADSeq）, sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS al- gorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm （GA）, simulated annealing （SA）, tabu search （TS） and particle swarm optimization （PSO）. Experimental results show that the developed algo-rithm outperforms these algorithms in terms of solution quality and efficiency.

A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

基于低碳物流的危化品仓库堆垛布局优化研究

为保证危化品仓库安全的同时有效减少碳排放并提升经济效益,建立危险指数最小、物料搬运量最小和碳排放成本最小的危化品仓库堆垛布局多目标优化模型,采用改进的粒子群-禁忌搜索混合算法对模型进行求解。该算法在传统粒子群算法的基础上加入多点变异操作,并在粒子群算法得出解的基础上加入禁忌搜索算法,提高算法跳出局部最优解的能力。研究结果表明:利用本文建立的多目标优化模型及改进算法,危险指数、物料搬运量和碳排放成本均有所下降,解集质量较高,从而在保证危化品安全的情况下,有效降低物料搬运量及碳排放成本。研究结果可为危化品企业对仓库内部碳排放量的影响因素和数值计算以及危化品仓库安全性的界定提供参考与借鉴。

基于改进粒子群算法的木材板材下料方法

木材板材在家具行业应用广泛,以绿色环保、节约能源为目的的木材板材优化下料已经成为研究的热点。木材板材下料优化问题属于二维矩形下料问题,是一种具有高度计算复杂性的问题。本研究主要针对单规格木材板材进行矩形零件下料问题,在木材板材长和宽都大于零件长和宽的情况下,通过建立二维下料的数学模型,采用标准粒子群算法、变邻域搜索算法、粒子群混合变邻域搜索算法分别进行求解,并以某企业的下料实例进行分析计算。首先,利用标准粒子群算法求解单规格板材下料问题;其次,利用变邻域搜索算法求解单规格板材下料问题。在获得局部最优解的基础上改变其邻域结构再进行局部搜索,找到另一个局部最优解,如此不断迭代,直到满足算法的终止条件,获得全局最优解;最后,利用粒子群变邻域搜索混合算法求解单规格板材下料问题。针对粒子群算法局部搜索能力较差、容易过早收敛的问题和具有较好包容性的特点,将变邻域搜索的思想融入粒子群算法中,使结果更加趋向全局最优。结果表明:粒子群变邻域搜索混合算法相比粒子群算法和变邻域算法效率都有显著提升,能显著提高该木材板材的利用率,增加企业经济效益。

基于差分进化粒子群混合算法的多无人机协同区域搜索策略

为提高无人机群在未知环境中的区域搜索效率,提出一种多无人机协同区域搜索策略。首先,根据区域搜索任务需求,建立包含区域覆盖率、区域不确定度、目标存在概率三种属性的区域信息地图;其次,以最大化搜索效率、同时最小化无人机搜索过程中的能耗为目标,建立无人机区域搜索滚动时域优化目标函数,指导无人机在线决策搜索路线;然后针对传统群智能优化算法易陷入局部最优的缺陷,设计差分进化粒子群混合算法在线求解该多目标优化问题,提高算法的寻优性能,从而提高无人机的搜索效率。最后,通过数值仿真实验,对所提算法进行验证,仿真结果表明,文中设计的基于差分进化粒子群混合算法的多无人机协同区域搜索策略与传统的群智能优化算法相比具有更高的区域搜索效率。

考虑碳交易机制的海港综合能源系统电-热混合储能优化配置

随着港口电气化进程逐渐加速,单一的港口供能方式正在向多种能源深度融合演变.为响应我国“碳达峰、碳中和”战略目标,进一步提升海港综合能源系统的经济与环境双重效益,提出一种考虑碳交易机制的电-热混合式储能优化配置方案.首先,建立海港综合能源系统模型,并给出计及碳交易市场的交易方案;其次,构建双层优化配置框架,上层优化配置混合式储能容量,下层引入碳交易机制,满足港口综合能源系统低碳经济运行需求;最后,结合网格自适应直接搜索法与自适应混沌粒子群算法优势,利用混合式优化算法对双层优化模型进行求解.以天津港的实际运行数据为例,验证该方法的有效性.算例结果表明,所提方法不仅可以降低系统的投入成本,还能显著减少港区碳排放,从而进一步提升港口经济和环境效益.

基于改进人工鱼群算法的蠕虫机器人路径规划

针对人工鱼群算法在机器人路径规划中存在路径长、精度不高、易陷入局部最优等问题,提出了一种改进的人工鱼群算法,旨在提高算法效率及精度。首先,在算法觅食行为中加入寻优循环,减少算法在路径规划中选取位置点的随机性,使机器人能够更快地走向目标点;其次,融合禁忌搜索算法,通过引入禁忌表来记录算法陷入局部最优的路径,使算法在选取新位置点时能够避开局部最优区域,避免算法在局部过度循环,同时对规划出的路径进行优化处理,删去重复栅格点之间的路径,保证路径中没有重复的栅格点;最后,将改进后的人工鱼群算法应用在一种新型的三维栅格地图中。实验结果表明:相较于其他对比算法,在地图1、2、3中改进人工鱼群算法所取得的平均路径长度分别减少了10%、15%、30%,在复杂地图中路径规划的成功率提高了75%。

Genetic Tabu Search for the Multi-Objective Knapsack Problem

We introduce a hybrid algorithm for the 01 multidimensional multi-objective knapsack problem. This algorithm, called GTS MOKP, combines a genetic procedure and a tabu search operator. The algorithm is evaluated on 9 well-known benchmark instances and shows highly competitive results compared with two state-of-the-art algorithms.

求解最小支配集问题的禁忌遗传混合算法

将最小支配集问题转换为一系列判定问题k支配集问题,并提出一种禁忌遗传混合算法对k-DS问题进行求解。此算法将禁忌搜索算法和遗传算法两种启发式算法结合起来,互补不足。高效的邻域结构保证了算法的运行效率,禁忌策略防止算法过早陷入局部最优陷阱,遗传算法框架进一步增强了算法的疏散性。经过与现有求解最小支配集算法的结果进行分析比较,禁忌遗传混合算法的结果较其它算法更优。

基于改进二进制PSO配电网动态重构

随着分布式电源的不断接入,配电网中的负荷和电源都在不断地变化,传统的计算方法存在收敛速度慢,易陷入局部最优的问题。本文通过应用信息熵对日负荷曲线进行时段划分,以网络损耗最小为目标函数建立配电网重构模型,针对传统PSO收敛较慢,易陷入局部最优的问题,提出结合禁忌搜索算法对PSO进行改进,通过设置禁忌表提高算法全局搜索能力;结合破圈法对拓扑结构进行约束,减少无效拓扑的出现,加快算法的收敛速度。通过IEEE33节点配电系统重构仿真,验证改进粒子群算法的可行性和有效性。

穿浪双体船纵向减摇策略麻雀搜索优化

为了改善穿浪双体船恶劣工况下的纵向稳定性,本文基于T型水翼及尾板设计了综合姿态控制系统。同时,对该系统相关控制参数的优化策略进行研究,以更好地实现减摇目标。采用了线性二次调节器和混合麻雀算法相结合的策略来设计控制器,将一种新型群智能算法(麻雀算法)及其改进算法应用于线性二次调节控制器权重矩阵寻优。通过两者对比,不仅验证了所设计姿态控制系统的有效性,也验证了该智能算法改进策略的高效性。结果表明:基于混合麻雀算法的控制器具有很好的自适应能力,且寻优过程收敛速度快,精度高,对工程实践具有一定的参考价值。

考虑风光消纳和碳减排的配电网混合储能优化配置研究

大力发展风电、光伏等可再生能源是我国实现“双碳”目标的重要途径。风光等分布式电源(distributed generation,DG)大量接入配电网后,将储能电池作为灵活性调节资源,可提升配电网风光消纳、碳减排和运行稳定性能力。考虑碳排放强度提出了碳排放指标,建立了考虑风光消纳和碳减排的配电网混合储能(hybrid energy storage,HES)优化配置模型;并通过禁忌搜索(tabu search,TS)和改进学习因子粒子群优化算法对模型进行求解;最后,通过IEEE-33节点配电网系统进行仿真分析,验证了所提方法的有效性。结果表明,合理配置混合储能可以有效提高配电网风光消纳能力、碳减排能力和运行稳定性。

考虑能源环境效益的含风电场多目标优化调度

随着大规模风电场的并网运行,电力系统调度过程需要考虑风电的影响;而日益严重的气候变化问题和人类社会可持续发展战略,也对电力行业提出了清洁化发展的要求。在传统电力系统优化调度的基础上,引入"能源环境效益"概念对包含风电场的电力系统优化调度模型进行修正,同时考虑发电资源消耗最少、能源环境效益最好、系统安全稳定程度最高等因素,提出了含风电场的多目标优化调度模型。在求解模型时采用模糊化处理技术,并提出了综合禁忌搜索思想的改进粒子群算法。实例结果表明,所提出的优化调度模型合理、算法可行。

免疫禁忌混合智能优化算法在配电网检修优化中的应用

从配电网设备检修计划编制的实际需要出发,建立了考虑多种约束条件、以配电网经济性最好为目标的检修计划优化模型。针对该模型的特点,提出了一种充分结合免疫算法与禁忌搜索算法优点的混和优化策略,该策略针对配电网检修计划优化问题的特点设计了3种疫苗,并且构造了2阶段变异,在优化前期使用禁忌搜索变异算子,而在优化后期恢复为一般变异算子,从而保证了算法的快速收敛。通过实际计算和分析,验证了文中所提出模型和算法的正确性和实用性,以及与遗传禁忌混合智能算法相比的优越性,实例计算结果表明本文所采用的方法是有效的,免疫禁忌混合智能算法在收敛速度,爬山能力,解的质量和稳定性上都要优于遗传禁忌组合算法,更适合求解配电网检修优化问题。