Chan-Cheng Liu

Efficient Population Utilization Strategy for Particle Swarm Optimizer

The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.

Effective Learning Rate Adjustment of Blind Source Separation Based on an Improved Particle Swarm Optimizer

Blind source separation (BSS) is a technique used to recover a set of source signals without prior information on the transformation matrix or the probability distributions of the source signals. In previous works on BSS, the choice of the learning rate would result in a competition between stability and speed of convergence. In this paper, a particle swarm optimization (PSO)-based learning rate adjustment method is proposed for BSS, and a simple decision-making method is introduced for how the learning rate should be applied in the current time slot. In the experiments, samples of four and ten source signals were mixed and separated and the results were compared with other related approaches. The proposed approach exhibits rapid convergence, and produces more efficient and more stable independent component analysis algorithms, than other related approaches.

Solving large scale global optimization using improved Particle Swarm Optimizer

As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer (EPUS-PSO). This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on 7 CEC 2008 test functions to present solution searching ability of the proposed method.

An improved multi-objective particle swarm optimizer for multi-objective problems

This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into local optimum. Experiments were conducted on eight common multi-objective benchmark problems. The results showed that the proposed method operates better in five performance metrics when solving these benchmark problems compared to three other related works.

A novel self-constructing Radial Basis Function Neural-Fuzzy System

This paper proposes a novel self-constructing least-Wilcoxon generalized Radial Basis Function Neural-Fuzzy System (LW-GRBFNFS) and its applications to non-linear function approximation and chaos time sequence prediction. In general, the hidden layer parameters of the antecedent part of most traditional RBFNFS are decided in advance and the output weights of the consequent part are evaluated by least square estimation. The hidden layer structure of the RBFNFS is lack of flexibility because the structure is fixed and cannot be adjusted effectively according to the dynamic behavior of the system. Furthermore, the resultant performance of using least square estimation for output weights is often weakened by the noise and outliers. This paper creates a self-constructing scenario for generating antecedent part of RBFNFS with particle swarm optimizer (PSO). For training the consequent part of RBFNFS, instead of traditional least square (LS) estimation, least-Wilcoxon (LW) norm is employed in the proposed approach to do the estimation. As is well known in statistics, the resulting linear function by using the rank-based LW norm approximation to linear function problems is usually robust against (or insensitive to) noises and outliers and therefore increases the accuracy of the output weights of RBFNFS. Several nonlinear functions approximation and chaotic time series prediction problems are used to verify the efficiency of self-constructing LW-GRBFNIS proposed in this paper. The experimental results show that the proposed method not only creates optimal hidden nodes but also effectively mitigates the noise and outliers problems.

Heuristic wavelet shrinkage for denoising

Noise reduction without any prior knowledge of noise or signals is addressed in this study. Compared with conventional filters, wavelet shrinkage can respect this requirement to reduce noise from received signal in wavelet coefficients. However, wavelet threshold depends on an estimate of noise deviation and a weight relating signals length cannot be applied in every case. This paper uses particle swarm optimization (PSO) to explore a suitable threshold in a complete solution space, named PSOShrink. A general-purpose objective function which is derived from blind signal separation (BSS) theory is further proposed. In simulation, four benchmarks signals and three degrading degrees are testing; meanwhile, three existing algorithm with state-of-the-art are performed for comparison. PSOShrink can not only recovers source signals from a heavy blurred signal but also remains details of a source signal from a light blurred signal; moreover, it performs outstanding denoising in every simulation case.

Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions

The underdetermined blind source separation (BSS), which based on sparse representation, is discussed in this paper; moreover, some difficulties (or real assumptions) that were left out of consideration before are aimed. For instance, the number of sources, , is unknown, large-scale, or time-variant; the mixing matrix is ill-conditioned. For the proposed algorithm, in order to detect a time-variant mixing matrix, short-time Fourier transform is employed to segment received mixtures. Because is unknown, our algorithm use more estimates to find out the mixing vectors by particle swarm optimizer (PSO); and then, surplus estimates are removed by two proposed processes. However, the estimated accuracy of PSO will affect the correctness of extracting mixing vectors. Consequently, an improved PSO version called the cluster guide PSO (CGPSO) is further proposed according to the character of sparse representation. In simulations, several real assumptions that were less discussed before will be tested. Some representative BSS algorithms and PSO versions are compared with the CGPSO-based algorithm. The advantages of the proposed algorithm are demonstrated by simulation results.

Optimal UAV flight path planning using skeletonization and Particle Swarm Optimizer

The purpose of this paper is to search the best flight route efficiently for unmanned aerial vehicle (UAV) in the 3-dimention complicated topography. The proposed method for the best flight route is mainly utilizing evolutionary algorithm, and give the proper initial population of evolutionary algorithm through skeletonization, efficient pre-processing procedure. In order to provide a smooth flight route for UAV, this paper adopts B-spline Curve method. Several control points of B-spline Curve method must be determined to generate flight route. The best control points can be calculated by Particle Swarm Optimizer (PSO). In this paper, the initial population of PSO is provided by skeletonization. The skeletonization of pre-processing procedure mainly includes two parts: one is Skeletonization and the other is candidate path searching. The purpose of pre-processing procedure is to reduce computation time, to prevent search the best solutions aimless, and execute evolutionary process efficiently. This paper uses Matlab as the experiment environment. The results of the experiments present the proposed method can provide the best flight route for UAV efficiently.

Potential offspring production strategies: An improved genetic algorithm for global numerical optimization

In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve various global numerical optimization problems. The SEGA employs a proposed population manager to preserve chromosomes which are superior and to eliminate those which are worse. The population manager also incorporates additional potential chromosomes to assist the solution exploration, controlled by the current solution searching status. The SEGA also uses the proposed sharing concepts for cross-over and mutation to prevent populations from falling into the local minimal, and allows GA to easier find or approach the global optimal solution. All the three parts in SEGA, including population manager, sharing cross-over and sharing mutation, can effective increase new born offsprings solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multi-modal, expanded, and hybrid composition functions. The results showed that the SEGA displayed better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.

An efficient noise reduction algorithm using empirical mode decomposition and correlation measurement

Noise reduction has a lot attention no matter in practical applications or a signal processing research field. Recently, a novel denoisy method which removes noise from received signals by threshold operation on wavelet coefficients was developed and its efficiency has been confirmed. However, its definition of parameters is not general-purpose enough to deal with variant cases. In order to seek high quality to denoisy, this study introduces a frequency analysis tool, empirical mode decomposition (EMD), to separate received signal into several elements which are termed intrinsic mode functions (IMF). And then, according to the order from high frequency to low frequency, IMFs could be separated into finite pair consisting of estimated noise and estimated original signal. Each estimated pair is calculated the correlation measurement which involves second-order correlation and high-order correlation since original signal and noise are mutually independent. A smallest measure value implies an optimal pair approximating to the real. In simulations, four benchmarks and three noise level are tested; moreover, two state of the art algorithms are compared with the proposed method. Finally, the excellent robustness and efficiency of proposed method are demonstrated by simulation results.