IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

求解旅行商问题的混合粒子群优化算法

结合遗传算法、蚁群算法和模拟退火算法的思想，提出用混合粒子群算法来求解著名的旅行商问题．与模拟退火算法、标准遗传算法进行比较，24种混合粒子群算法的效果都比较好，其中交叉策略D和变异策略F的混合粒子群算法的效果最好，而且简单有效．对于目前仍没有较好解法的组合优化问题，通过此算法修改很容易解决．     

A study on finite-time particle swarm optimization as a system identification method

Microsystem Technologies - The performance as a system identification technique of a variant of the particle swarm optimization (PSO) algorithm named finite-time particle swarm optimization (FPSO)...     

Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization

Solving the multi-stage portfolio optimization (MSPO) problem is very challenging due to nonlinearity of the problem and its high consumption of computational time. Many heuristic methods have been employed to tackle the problem. In this paper, we propose a novel variant of particle swarm optimization (PSO), called drift particle swarm optimization (DPSO), and apply it to the MSPO problem solving. The classical return-variance function is employed as the objective function, and experiments on the problems with different numbers of stages are conducted by using sample data from various stocks in S&P 100 index. We compare performance and effectiveness of DPSO, particle swarm optimization (PSO), genetic algorithm (GA) and two classical optimization solvers (LOQO and CPLEX), in terms of efficient frontiers, fitness values, convergence rates and computational time consumption. The experiment results show that DPSO is more efficient and effective in MSPO problem solving than other tested optimization tools.     

用并行化的QPSO解决有约束的优化问题

采用粒子群系统的并行化的量子化模型提高全局搜寻能力,在解决约束问题时采用不固定的多阶段任务补偿函数以提高收敛性,并获得更准确的结果,提出了并行化的QPSO（PQPSO）算法。此算法在几个可信赖的基准函数中被测试,并且实验结果显示PQPSO的最优值和运行时间比QPSO和传统的PSO有很大的提高,而且运行所用的时间资源接近线性减少。     

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15–70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.     

Parameters identification of chaotic systems by quantum-behaved particle swarm optimization

This paper applies a novel evolutionary optimization algorithm named quantum-behaved particle swarm optimization (QPSO) to estimate the parameters of chaotic systems, which can be formulated as a multimodal numerical optimization problem with high dimension from the viewpoint of optimization. Moreover, in order to improve the performance of QPSO, an adaptive mechanism is introduced for the parameter beta of QPSO. Finally, numerical simulations are provided to show the effectiveness and efficiency of the modified QPSO method.     

0-1背包问题的模糊粒子群算法求解*

针对基本粒子群算法在背包问题上表现的不足,在基本粒子群算法的基础上运用模糊规则表加入了新的扰动因子,提出了一种新的算法——模糊粒子群算法。该算法结合了模糊控制器中输入/输出的模糊化处理和粒子群寻优的特点,为实际问题提供了新的解决手段。将模糊粒子群算法应用于0-1背包问题上,通过多组实例数据进行测试,验证表明了本算法具有良好的有效性和鲁棒性。     

自适应粒子群算法求解Agent联盟

联盟生成是多Agent系统的一个关键问题,主要研究如何在多Agent系统中动态生成面向任务的最优Agent联盟.引入粒子群算法来解决这一问题,受到惯性权重c0在进化过程中所起作用的启发,引入自适应惯性权重cadp对粒子群算法进行改进,使其不再易于陷入局部极小.对比实验结果表明,该算法在解的性能和收敛速度上均优于相关算法.     

基于离散微粒群算法求解背包问题研究

微粒群算法(PSO)是一种新的演化算法,主要用于求解数值优化问题.基于离散微粒群算法(DPSO)分别与处理约束问题的罚函数法和贪心变换方法相结合,提出了求解背包问题的两个算法:基于罚函数策略的离散微粒群算法(PFDPSO)和基于贪心变换策略的离散微粒群算法(GDPSO).通过将这两个算法与文献[7]中的混合微粒群算法(Hybrid_PSO)进行数值计算比较发现:对于求解大规模的背包问题,GDPSO非常优秀,其求解能力优于Hybrid_PSO和PFDPSO,是求解背包问题的一种非常有效的方法.     

基于高斯微粒群优化的动态神经网络延迟系统辨识

为提高神经网络对未知非线性大滞后动态系统的泛化能力,提出一种基于高斯微粒群优化的自适应动态前馈神经网络.在输入层与隐含层之间、隐含层与输出层之间分别加入动态延迟算子,可以高效地辨识出系统纯滞后时间,建立精确系统模型.此外,采用高斯函数和混沌映射方法平衡微粒群算法全局寻优能力,以克服提前收敛的缺陷,从而快速有效地自适应优化网络中的参数.仿真实验表明了该方法在非线性人滞后系统辨识中的有效性.     

Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers.     

Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization

With the improvement of the aircraft flight performance and development of computing science,uninhabited combat aerial vehicle(UCAV) could accomplish more complex tasks.But this also put forward stricter requirements for the flight control system,which are the crucial issues of the whole UCAV system design.This paper proposes a novel UCAV flight controller parameters identification method,which is based on predator-prey particle swarm optimization(PSO) algorithm.A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper,and a predator-prey PSO-based software platform for UCAV controller design is also developed.     

Solving the bi-objective personnel assignment problem using particle swarm optimization

A particle swarm optimization (PSO) algorithm combined with the random-key (RK) encoding scheme (named as PSORK) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The main contribution of this work is to improve the f₁_f₂ heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f₁_f₂ heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f₁, and f₂ for the personnel assignment problem. In this paper, PSORK algorithm searches the solution of BOPAP space thoroughly. The experimental results show that the solution quality of BOPAP based on the proposed method is far better than that of the f₁_f₂ heuristic algorithm.     

基于P系统的粒子群优化算法

为了克服算法早熟收敛问题并提高算法精度, 引入了膜计算理论。将PSO算法与P系统相结合, 提出了一种基于P系统的粒子群优化算法 (P-PSO), 有效地平衡粒子群的全局搜索和局部寻优。采用常用的三个测试函数对新算法进行了实验, 结果表明, 提出的P-PSO算法有效地解决了算法早熟问题, 提高了算法的收敛精度。由此可见, P-PSO算法能够有效改进原有PSO算法的性能。     

Solving the Urban Transit Routing Problem using a particle swarm optimization based algorithm

The Urban Transit Routing Problem (UTRP) comprises an NP-hard problem that deals with the construction of route networks for public transit networks. It is a highly complex and multiply constrained problem, in which the assessment of candidate route networks can be both time consuming and challenging. Except for that, a multitude of potential solutions are usually rejected due to infeasibility. Because of this difficulty, soft computing algorithms can be very effective for its efficient solution. The success of these methods, however, depends mainly on the quality of the representation of candidate solutions, on the efficiency of the initialization procedure and on the suitability of the modification operators used.An optimization algorithm, based on particle swarm optimization, is designed and presented in the current contribution, aiming at the efficient solution of UTRP. Apart from the development of the optimization algorithm, emphasis is also given on appropriate representation of candidate solutions, the route networks in other words, and the respective evaluation procedure. The latter procedure considers not only the quality of service offered to each passenger, but also the costs of the operator. Results are compared on the basis of Mandl's benchmark problem of a Swiss bus network, which is probably the only widely investigated and accepted benchmark problem in the relevant literature. Comparison of the obtained results with other results published in the literature shows that the performance of the proposed soft computing algorithm is quite competitive compared to existing techniques.     

Power system aggregate load area modelling by particle swarm optimization

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.     

IIR model identification using a modified inclined planes system optimization algorithm

Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding motion dynamic along a frictionless inclined surface. In this paper, with the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The IIR model identification is a complex and practical challenging problem due to multimodal error surface entanglement that many researches have been reported for it. In this work, MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors. To do this, unknown filter parameters are considered as a vector to be optimized. In implementation, at first, to demonstrate the effectiveness of the proposed method, 10 well-known benchmark functions have been considered for evaluating and testing. In addition, statistical analysis on the powerfulness, efficiency and applicability of the MIPO algorithm are presented. Obtained results in compared to some other popular methods, confirm the efficiency of the MIPO algorithm that makes the best optimal solutions and has a better performance and acceptable solutions.     

Instance variant nearest neighbor using particle swarm optimization for function approximation

In this paper, a new approach called ‘instance variant nearest neighbor’ approximates a regression surface of a function using the concept of k nearest neighbor. Instead of fixed k neighbors for the entire dataset, our assumption is that there are optimal k neighbors for each data instance that best approximates the original function by fitting the local regions. This approach can be beneficial to noisy datasets where local regions form data characteristics that are different from the major data clusters. We formulate the problem of finding such k neighbors for each data instance as a combinatorial optimization problem, which is solved by a particle swarm optimization. The particle swarm optimization is extended with a rounding scheme that rounds up or down continuous-valued candidate solutions to integers, a number of k neighbors. We apply our new approach to five real-world regression datasets and compare its prediction performance with other function approximation algorithms, including the standard k nearest neighbor, multi-layer perceptron, and support vector regression. We observed that the instance variant nearest neighbor outperforms these algorithms in several datasets. In addition, our new approach provides consistent outputs with five datasets where other algorithms perform poorly.