Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R ²). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.     

改进型帝国竞争模型算法的研究

为了改善帝国竞争算法（Imperialist Competitive Algorithm,ICA）易早熟收敛,搜索范围低,精度小,帝国之间信息交互性不强等缺点,提出了两种基于同化模型和竞争模型的改进的ICA算法。针对殖民地在移动过程中由于过于直接的靠近统治者而造成的搜索范围过小以及容易陷入局部最优的情况在同化过程中引入了差异因子来增大搜索范围。针对帝国之间的交互性的缺失,引入了人忠诚度的算子来实现帝国交互以及同化机制的模型改变,较强的帝国统治者会因为忠诚度算子获得更多的支持,从而细致划分了一个帝国中的每个国家,利用纳什均衡和最大最小公平性引导帝国竞争进而使算法向最优解进行搜索。在竞争过程中设置时间节点动态划分迭代阶段,根据迭代的不同阶段特点选择最优竞争系数。对算法进行了理论证明,最后将算法应用于多个函数进行检测并与其他的改进ICA算法进行比较,在搜索精度和范围广度上有了一定的提高。     

FICA: fuzzy imperialist competitive algorithm

Despite the success of the imperialist competitive algorithm （ICA） in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step;according to a fuzzy membership function, other countries become colonies of all the empires. In ab-sorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated;instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.     

Barrier coverage of WSNs with the imperialist competitive algorithm

Barrier coverage in wireless sensor networks has been used in many applications such as intrusion detection and border surveillance. Barrier coverage is used to monitor the network borders to prevent intruders from penetrating the network. In these applications, it is critical to find optimal number of sensor nodes to prolong the network lifetime. Also, increasing the network lifetime is one of the important challenges in these networks. Various algorithms have been proposed to extend the network lifetime while guaranteeing barrier coverage requirements. In this paper, we use the imperialist competitive algorithm (ICA) for selecting sensor nodes to do barrier coverage monitoring operations called ICABC. The main objective of this work is to improve the network lifetime in a deployed network. To investigate the performance of ICABC, several simulations were conducted and the results of the experiments show that the ICABC significantly improves the performance than other state-of-art methods.     

基于帝国主义竞争算法的WSNs定位方案

遗传算法(GA)在无线传感器网络(WSNs)定位时存在收敛速度慢、精度低等弊端,针对以上问题,提出了一种利用帝国主义竞争算法(ICA)优化WSNs定位的方案。首先,使用了采样的方法来估计未知节点的初始位置;其次,依靠信标节点和相邻节点的相关信息建立了以最小化全局误差的三维空间的数学定位模型;最后,使用了最新的社会启发算法—ICA来进行定位优化。实验结果表明:与GA定位相比,ICA在WSNs定位上具有定位精度高、收敛迅速的优势。     

Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm

This study attempted to predict corrosion current density in concrete using artificial neural networks (ANN) combined with imperialist competitive algorithm (ICA) used to optimize weights of ANN. For that reason, temperature, AC resistivity over the steel bar, AC resistivity remote from the steel bar, and the DC resistivity over the steel bar are considered as input parameters and corrosion current density as output parameter. The ICA–ANN model has been compared with the genetic algorithm to evaluate its accuracy in three phases of training, testing, and prediction. The results showed that the ICA–ANN model enjoys more ability, flexibility, and accuracy.     

一种求解旅行商问题的新型帝国竞争算法

帝国竞争算法是一种已在连续优化问题上取得较好效果的新型社会政治算法. 为了使该算法更好地应用于离散型组合优化问题, 提出一种求解旅行商问题的新型帝国竞争算法. 在传统算法的基础上, 改变初始帝国的生成方式; 同化过程采取替换重建方式, 以提升求解质量; 革命过程中引入自适应变异算子, 以增强搜索能力; 殖民竞争过程中调整了殖民地分配方式; 算法加入帝国增强过程, 以加快寻化速度. 实验结果表明, 新型帝国竞争算法求解质量高、收敛速度快.

     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。     

求解约束优化问题的新型帝国竞争算法

针对约束优化问题,提出一种约束处理的新策略,运用字典序方法同时优化问题的目标函数和约束违背程度,设计一种新型帝国竞争算法.该算法给出成本和归一化成本的新定义,以避免殖民国家势力为零,并应用嵌入殖民地间全局搜索的同化、基于优秀殖民地的革命、殖民国家的差分进化和新型帝国竞争等策略提高求解质量.基于两组约束优化标准测试函数的实验结果和算法对比表明,结合字典序方法的新型帝国竞争算法在约束优化问题的求解方面具有较强的优势.     

离散型帝国竞争算法在仓储订单调度中的应用

针对自动化立体仓储的订单批量处理过程中的优化调度问题, 采用离散型帝国竞争算法对订单调度流程进行求解, 构建出库订单优先调度和复合订单调度两种对比流程来进行优劣势的探讨以及算法比较. 调度实例求解结果表明, 所提出算法的优化质量更高, 且在处理大批量复合订单时更具有优越性, 从而验证了所提出算法的有效性和优越性.     

Structural damage detection using imperialist competitive algorithm and damage function

In practical damage detection problems, experimental modal data is only available for a limited number of modes and in each mode, only a limited number of nodal points are recorded. In using modal data, the majority of the available damage detection solution techniques either require data for all the modes, or all the nodal data for a number of modes; neither of which may be practically available through experiments. In the present study, damage identification is carried out using only a limited number of nodal data of a limited number of modes. The proposed method uses the imperialist competitive optimization algorithm and damage functions. To decrease the number of design variables, several bilinear damage functions are defined to model the damage distribution. Damage functions with both variable widths and variable weights are proposed for increased accurately. Four different types of objective functions which use modal responses of damaged structure are investigated with the aim of finding the most suitable function. The efficiency of the proposed method is investigated using three benchmark numerical examples using both clean and noisy modal data. It is shown that by only using a limited number of modal data, the proposed method is capable of accurately detecting damage locations and reasonably accurately evaluate their extents. The proposed algorithm is most effective with noisy modal data, compared to other available solutions.     

Chaotic imperialist competitive algorithm for optimum design of truss structures

The imperialist competitive algorithm is a new socio-politically motivated optimization algorithm which recently is applied for structural problems. This paper utilizes the idea of using chaotic systems instead of random processes in the imperialist competitive algorithm. The resulting method is called chaotic imperialist competitive algorithm (CICA) in which chaotic maps are utilized to improve the movement step of the algorithm. Some well-studied truss structures are chosen to evaluate the efficiency of the new algorithm.     

柔性车间调度问题的协作混合帝国算法

针对柔性车间调度问题（FJSP）的非确定性多项式特性,提出一种新的改进算法——协作混合帝国算法,用于寻找最小化最大完工时间的调度。首先,根据标准帝国竞争算法（ICA）的流程特性,设计了自适应参数的改进,可提高算法的收敛速度;然后,引入帝国和殖民地双改革变异,并针对工序排序和选择机器的不同阶段提出多变异改革策略,可提高算法的局部搜索效率;最后,创建大陆间国家交流合作机制,促进优秀国家对外信息交流,可提高算法全局搜索能力。通过对多个柔性车间调度实例进行仿真,结果表明,所提出算法在求解质量和稳定性上均优于多种群体智能进化算法,更适合解决该类调度问题。     

受春秋战国史实启发的帝国竞争改进算法

针对帝国竞争算法过早收敛导致的求解高维函数时易陷入维数灾难的问题,受我国春秋战国时期诸侯国争雄称霸史实启发,提出了一种改进的帝国竞争算法.首先,在初始化国家阶段引入"合纵连横"竞争机制,以增强信息交互,保留较优种群;其次,在帝国同化过程中借鉴由国家各层面逐步渗透同化的殖民统治策略,以提升算法的开发能力;最后,加入判断并...     

基于新型帝国竞争算法的高维多目标柔性作业车间调度

针对高维多目标柔性作业车间调度问题(MaOFJSP),提出了一种新型帝国竞争算法(ICA)以同时最小化最大完成时间、最大拖期、最大机器负荷和总能耗,该算法采用新方法构建初始帝国使得大多数殖民国家分配数量相近的殖民地,引入殖民国家的同化,并应用新的革命策略和帝国竞争方法以获得高质量解.最后通过大量实验测试ICA新策略对其性能的影响并将ICA与其他算法对比,实验结果表明新型ICA在求解MaOFJSP方面具有较强的优势.     

An imperialist competitive algorithm for a mixed-model assembly line sequencing problem

This paper presents a novel imperialist competitive algorithm (ICA) to a just-in-time (JIT) sequencing problem where variations of production rate are to be minimized. This type of problem is NP-hard. Up to now, some heuristic and meta-heuristic approaches are proposed to minimize the production rates variation. This paper presents a novel algorithm for optimization which inspired by imperialistic competition in real world. Sequences of products where minimize the production rates variation is desired. Performance of the proposed ICA was compared against a genetic algorithm (GA) in small, medium and large problems. Experimental results show the ICA performance against GA.     

An imperialist competitive algorithm for virtual machine placement in cloud computing

Abstract

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user’s applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.     

Improved imperialist competitive algorithm with mutation operator for continuous optimization problems

Imperialist competitive algorithm is a nascent meta-heuristic algorithm which has good performance. However, it also often suffers premature convergence and falls into local optimal area when employed to solve complex problems. To enhance its performance further, an improved approach which uses mutation operators to change the behavior of the imperialists is proposed in this article. This improved approach is simple in structure and is very easy to be carried out. Three different mutation operators, the Gaussian mutation, the Cauchy mutation and the Lévy mutation, are investigated particularly by experiments. The experimental results suggest that all the three improved algorithms have faster convergence rate, better global search ability and better stability than the original algorithm. Furthermore, the three improved algorithms are also compared with other two excellent algorithms on some benchmark functions and compared with other four existing algorithms on one real-world optimization problem. The comparisons suggest that the proposed algorithms have their own specialties and good applicability. They can obtain better results on some functions than those contrastive approaches.

     

Data clustering based on hybrid K-harmonic means and modifier imperialist competitive algorithm

Clustering is a process for partitioning datasets. Clustering is one of the most commonly used techniques in data mining and is very useful for optimum solution. K-means is one of the simplest and the most popular methods that is based on square error criterion. This algorithm depends on initial states and is easily trapped and converges to local optima. Some recent researches show that K-means algorithm has been successfully applied to combinatorial optimization problems for clustering. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. In this article, we purpose a novel method that is based on combining two algorithms; K-harmonic means and modifier imperialist competitive algorithm. It is named ICAKHM. To carry out this experiment, four real datasets have been employed whose results indicate that ICAKHM. Four real datasets are employed to measure the proposed method include Iris, Wine, Glass and Contraceptive Method Choice with small, medium and large dimensions. The experimented results show that the new method (ICAKHM) carries out better results than the efficiency of KHM, PSOKHM, GSOKHM and ICAKM methods.