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Abstract-A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neuralnetwork-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. Firstly we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding.The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures -thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length and network diameter are highly sensitive to the interior crisis captured in this particular data set. 1 arXiv:1501.06656v1 [nlin.CD] 27 Jan 2015Within the last ten years a novel approach to time series analysis has emerged whereby data is transformed into a complex network and then analysed using various measures from network science. The choice of transformation algorithm is critical in this process as different methods are inherently more effective at capturing certain aspects of dynamics and less effective at capturing others. In this paper we investigate a recently proposed algorithm known as the method of ordinal partitions. This computationally simple algorithm explicitly embeds temporal information in the network structure by partitioning the time series into a set of symbolic states which become network nodes, and then connecting these nodes based on the transition sequence present in the data. New in this work, we generalise the algorithm by introducing a time lag parameter for the elements in each partition, as is done in traditional methods of time delay embedding. Our results demonstrate that this new approach generates networks which are measurably sensitive to the dynamics present in the source time series, and has the potential to be useful as a tool for change point detection in continuous chaotic systems.

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An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Modeling the development of Fluid Dispensing for Electronic Packaging (MFD-EP) is presented in this paper. In modeling the fluid dispensing process, it is important to understand the process behavior as well as determine the optimum operating conditions of the process for a high-yield, low-cost, and robust operation. Modeling the fluid dispensing process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.Index Terms-Modeling, particle swarm optimization (PSO), wavelet neural network (WNN), wavelet theory.