Particle Swarm Genetic Algorithm Research Articles

Optimal linear phase finite impulse response band pass filter design using craziness based particle swarm optimization algorithm

An efficient method is proposed for the design of finite impulse response (FIR) filter with arbitrary pass band edge, stop band edge frequencies and transition width. The proposed FIR band stop filter is designed using craziness based particle swarm optimization (CRPSO) approach. Given the filter specifications to be realized, the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics. In this paper, for the given problem, the realizations of the optimal FIR band pass filters of different orders have been performed. The simulation results have been compared with those obtained by the well accepted evolutionary algorithms, such as Parks and McClellan algorithm (PMA), genetic algorithm (GA) and classical particle swarm optimization (PSO). Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality.

Adaptive remote sensing image fusion under the framework of data assimilation

The existing fusion methods cannot adjust fused images, adaptively according to the requirements of follow-up image processing steps, as well as the merits of different fusion methods, and are not easy to be integrated. Since a data assimilation system can integrate the advantage of a model operator and observer operator, a fusion framework based on the data assimilation concept is proposed, which can adaptively fuse different remote sensing images. Under this framework, a fusion method based on an independent component analysis and àtrous wavelet transform is used as a model operator, and another fusion method based on nonsubsampled contourlet transform is used as an observation operator. Meanwhile, image quantitative evaluation indicators are used as an objective function. Then, the genetic particle swarm algorithm is employed to optimize the objective function in order to gain a more suitable image. Finally, three sets of panchromatic images and multispectral images are used in experiments. The results show that the proposed algorithm can adjust fusion results adaptively, according to a particular objective function.

Errata: Adaptive remote sensing image fusion under the framework of data assimilation

The existing fusion methods cannot adjust fused images, adaptively according to the requirements of follow-up image processing steps, as well as the merits of different fusion methods, and are not easy to be integrated. Since a data assimilation system can integrate the advantage of a model operator and observer operator, a fusion framework based on the data assimilation concept is proposed, which can adaptively fuse different remote sensing images. Under this framework, a fusion method based on an independent component analysis and atrous wavelet transform is used as a model operator, and another fusion method based on nonsubsampled contourlet transform is used as an observation operator. Meanwhile, image quantitative evaluation indicators are used as an objective function. Then, the genetic particle swarm algorithm is employed to optimize the objective function in order to gain a more suitable image. Finally, three sets of panchromatic images and multispectral images are used in experiments. The results show that the proposed algorithm can adjust fusion results adaptively, according to a particular objective function.

Identification of nonlinear systems using modified particle swarm optimisation: a hydraulic suspension system

This paper presents a novel modified particle swarm optimisation (MPSO) algorithm to identify nonlinear systems. The case of study is a hydraulic suspension system with a complicated nonlinear model. One of the main goals of system identification is to design a model-based controller such as a nonlinear controller using the feedback linearisation. Once the model is identified, the found parameters may be used to design or tune the controller. We introduce a novel mutation mechanism to enhance the global search ability and increase the convergence speed. The MPSO is used to find the optimum values of parameters by minimising the fitness function. The performance of MPSO is compared with genetic algorithm and alternative particle swarm optimisation algorithms in parameter identification. The presented comparisons confirm the superiority of MPSO algorithm in terms of the convergence speed and the accuracy without the premature convergence problem. Furthermore, MPSO is improved to detect any changes of system parameters, which can be used for designing an adaptive controller. Simulation results show the success of the proposed algorithm in tracking time-varying parameters.

Electromagnetic scattering~of metallic cylinders of arbitrary shape by using asynchronous particle swarm optimization and non-uniform steady state genetic algorithm

Two techniques for the shape reconstruction of multiple metallic cylinders from scattered fields are investigated in this paper, in which two-dimensional configurations are involved. After an integral formulation, the method of moment (MoM) is applied to solve it numerically. Two separate perfect-conducting cylinders of unknown shapes are buried in one half-space and illuminated by the transverse magnetic (TM) plane wave from the other half space. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equation is derived and the imaging problem is reformulated into optimization problem. The non-uniform steady state genetic algorithm (NU-SSGA) and asynchronous particle swarm optimization (APSO) are employed to find out the global extreme solution of the object function. Numerical results demonstrate even when the initial guesses are far away from the exact shapes, and the multiple scattered fields between two conductors are serious, good reconstruction can be obtained. In addition, the effect of Gaussian noise on the reconstruction results is investigated and the numerical simulation shows that the reconstruction results are good and acceptable, as long as the SNR is greater than 20 dB.

Application of simple examination and retrieval procedure in evolutionary algorithms for feeder reconfiguration

This paper presents an effective approach based on simple examination and retrieval procedure (SERP) that can be used in evolutionary algorithms, such as genetic algorithm (GA) and binary particle swarm optimization (BPSO), for solving distribution feeder reconfiguration problems. The proposed approach is able to improve the search efficiency by identifying and modifying the illegal chromosomes that represent switching operation plans. The concept of simple examination is done by checking the numbers of normally opened switches on a branchless line segment in distribution systems. By applying the proposed approach, the number of illegal chromosomes during the regeneration process in evolutionary algorithms can be reduced. The idea of retrieval procedure is done by trying to use the information from historical local optimal solutions to repair the illegal chromosomes as many as possible. A 33-Bus distribution system and a practical distribution system of Taiwan Power Company (TPC) are used in this paper to verify and validate the effectiveness of the proposed procedure. The simulations consider the distribution system with and without distributed generation (DG) for power losses problems. Simulation results show that integration of the proposed procedure to GA and PSO provides more efficient and stable solutions for feeder reconfiguration problems. Copyright © 2010 John Wiley & Sons, Ltd.

The Scheduling Algorithm of Grid Task Based on PSO and Cloud Model

Grid task scheduling (GTS) is a NP-hard problem. This paper proposes an optimized GTS algorithm which combines with the advantages of cloud model based on the particle swarm optimization algorithm. This algorithm iterates tasks utilizing the advantages of particle swarm optimization algorithm and then gets a set of candidate solutions quickly. In addition, this algorithm modifies the value of entropy and excess entropy using the characteristics of cloud model and implements the transformation between qualitative variables and quantity of uncertain events. And this algorithm makes particles fly to the global optimal solutions by exact searching in local areas. Theoretical analysis and simulation results show that this algorithm makes load balance of resource efficiently. It also avoids the problems of genetic algorithm and basic particle swarm optimization algorithm, which would easily fall into local optimal solutions and premature convergence caused by too much selected pressure. This algorithm has the advantages of high precision and faster convergence and can be applied in task scheduling on computing grid.

Comparison of genetic and binary particle swarm optimization algorithms on system maintenance scheduling using prognostics information

Recent technical advances in condition-based maintenance technology have made it possible to not only diagnose existing failures, but also forecast future failures, which is called prognostics. A common method of maintenance scheduling in condition-based maintenance is to apply thresholds to prognostics information, which is not appropriate for systems consisting of multiple serially connected machinery. Maintenance scheduling is defined as a binary optimization problem and has been solved with a genetic algorithm. In this article, various binary particle swarm optimization methods are analysed and compared with each other and a genetic algorithm on a maintenance-scheduling problem for condition-based maintenance systems using prognostics information. The trade-off between maintenance and failure is quantified as the risk to be minimized. The forecasted failure probability of serially connected machinery is utilized in the analysis of the whole system. In addition to the comparison of a genetic algorithm and binary particle swarm optimization methods, a new binary particle swarm optimization that combines the good sides of two binary particle swarm optimizations is presented.