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.     

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...     

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.     

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.     

An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm

The Journal of Supercomputing - Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in...     

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.     

基于改进帝国竞争算法的柔性作业车间机器故障重调度

针对机器故障下的柔性作业车间重调度问题,提出了一种改进的帝国竞争算法(ICA).首先,以最大完工时间、机器能耗和总延迟时间为目标函数建立柔性作业车间动态重调度模型,并对三个目标采用线性加权法;然后提出了改进的ICA来把优良的信息保留到下一代,即在传统ICA的同化和革命步骤后加入一个轮盘赌的选择机制,使初始帝国中的优秀基...     

Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization

System reliability analysis and optimization are important to efficiently utilize available resources and to develop an optimal system design architecture. System reliability optimization has been solved by using optimization techniques including meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research field of the reliability optimization wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic meta-heuristics, such as simulated annealing, genetic algorithm, tabu search, ant colony, and particle swarm optimization paradigms, has been developed for reliability-redundancy optimization of systems. Recently, a new kind of evolutionary algorithm called Imperialist Competitive Algorithm (ICA) was proposed. The ICA is based on imperialistic competition where the populations are represented by countries, which are classified as imperialists or colonies. However, the trade-off between the exploration (i.e. the global search) and the exploitation (i.e. the local search) of the search space is critical to the success of the classical ICA approach. An improvement in the ICA by implementing an attraction and repulsion concept during the search for better solutions, the AR-ICA approach, is proposed in this paper. Simulations results demonstrates the AR-ICA is an efficient optimization technique, since it obtained promising solutions for the reliability redundancy allocation problem when compared with the previously best-known results of four different benchmarks for the reliability-redundancy allocation problem presented in the literature.     

Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm

Maintenance activities have been ignored in many studies on scheduling problems where all machines are assumed to be available without interruption in the planning horizon. However, in realistic situations, they might be unavailable due to preventive maintenance, basic maintenance or unforeseen breakdowns. In this paper, we simulate a condition-based maintenance (CBM) for flexible job shop scheduling problem (FJSP) and consider the combination of Sigmoid function and Gaussian distribution to improve the CBM simulation. This study proposes an improved imperialist competitive algorithm (ICA) for the FJSP scheduling problem with the objective of the makespan minimization. The performance of the proposed algorithm is enhanced with a hybridization of ICA with simulated annealing (SA), after diagnosing standard ICA disadvantages and shortcomings. This ICA also includes a simulation part to handle CBM requirements. Various parameters of the novel ICA are reviewed to calibrate the algorithm with the help of the Taguchi experimental design. Experimental results show the high performance of the novel ICA in comparison with the standard ICA. The obtained results demonstrate that the novel ICA is an effective algorithm for FJSP under CBM. Finally, the performance of ICA is evaluated compared to other popular algorithms.     

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.