Conventional Particle Swarm Optimization Algorithm Research Articles

Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed. Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

Shuffled frog leaping algorithm optimization for AC--DC optimal power flow dispatch

In this paper, a simple-implemented and reliable AC{DC optimal power ow (OPF) is proposed. Although high-voltage direct current (HVDC) transmission lines are being increasingly used in power systems, new optimization algorithms such as evolutionary and memetic algorithms, which never stick in local minimum, so far have not been implemented in the AC{DC OPF problem. An evolutionary algorithm known as the shued frog leaping algorithm (SFLA) is proposed in this paper to solve the OPF dispatch in an AC{DC power system (including both high-voltage alternating current and HVDC transmission lines). The implementation of the AC{DC OPF with these kinds of methods is much simpler than that of traditional (numerical) algorithms. In order to prove the quality of the SFLA, the proposed method is applied to 2 case studies, including the Western System Coordinating Council 9-bus and IEEE 30-bus power systems, and compared with the conventional particle swarm optimization (PSO) algorithm and 3 of its modied versions, while the constraints are all satised. The results of comparison indicate that the SFLA is a reliable and fast optimization

A Particle Swarm Optimizer Mimicking Parasitic Immune Behavior

Inspired by the phenomenon of parasitic immune behavior in natural ecosystem, a novel Particle Swarm Optimization algorithm based on Parasitic Immune (PSOPI) is proposed for conventional Particle Swarm Optimization algorithms (PSO) often trapped in the local optima. In this paper, in order to improve the searching ability of the algorithm, we divided the particle swarm into two populations called as parasitic and host. A certain number of particles of two populations are exchanged according to the fitness values of each population after which is sorted in a given iterations. The particles are obtained by the parasite population with good fitness from the host population which is used to simulate the behavior of the parasite population got nourishments. As a result, the host population was harmed. Elitist learning strategy is applied to the particle of parasite population to avoid the elite particles trapped in the local optimum. Exploration strategy and the clonal selection of immune system were introduced into host population to expand the search space of solutions and inhibit the premature stagnation. Finally, the experimental results of a set of benchmark functions demonstrate the efficacy of the present algorithm.

Identification of Position DC Motor and Control Using Fuzzy Type 2 Based PSO

This paper presents identification of a position DC motor using least squares analysis to estimate the parameters of an ARX model .Type-2 fuzzy logic controllers is proposed as an alternative solution in the literature when a system has a large amount of uncertainties. This paper proposed an algorithm of interval fuzzy type 2 logic controller(PSOIT2FLC)with the optimized parameters using particle swarm optimization in the design of fuzzy controller for the position control of DC Motor. The performance of the proposed PSOIT2FLC is compared with that of its corresponding conventional PSO algorithm type -1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. . Extensive simulation studies are conducted to compare the response of the given system with the conventiona genetic algorithm type -1 fuzzy controller to the response given with the proposed PSOIT2FLC scheme. Simulation works in MATLAB environment demonstrate that the PSO optimized fuzzy position controller became very strong, gives better results and possesses good robustness.

Application and Comparison of PSO, its Variants and HDE Techniques to Emission/Economic Dispatch

The conventional particle swarm optimization (PSO) algorithm known for its global searching capabilities has been successfully applied to emission/economic dispatch (EED) problems. In this paper, the applicability of PSO and its four variants, namely, self-adaptive PSO, dispersed PSO, chaotic PSO and new PSO to emission/economic dispatch problems has been presented. Furthermore, the employability of hybrid differential evolution (HDE) technique to EED problems is also proved here. Two numerical examples are tested—one having 9 units, and limits on SO2 and NO x emissions and the other having 6 units with NO x emission and B-loss co-efficients. Results obtained are compared with those reported in literature. The comparison of the results shows that the conventional PSO, its variants and HDE algorithm converge to optimal or near optimal solutions in the two examples tested in this paper. But the convergence time in HDE is far greater when compared with PSO and its variants.

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

This paper proposes a novel multi-objective model for an unrelated parallel machine scheduling problem considering inherent uncertainty in processing times and due dates. The problem is characterized by non-zero ready times, sequence and machine-dependent setup times, and secondary resource constraints for jobs. Each job can be processed only if its required machine and secondary resource (if any) are available at the same time. Finding optimal solution for this complex problem in a reasonable time using exact optimization tools is prohibitive. This paper presents an effective multi-objective particle swarm optimization (MOPSO) algorithm to find a good approximation of Pareto frontier where total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously. The proposed MOPSO exploits new selection regimes for preserving global as well as personal best solutions. Moreover, a generalized dominance concept in a fuzzy environment is employed to find locally Pareto-optimal frontier. Performance of the proposed MOPSO is compared against a conventional multi-objective particle swarm optimization (CMOPSO) algorithm over a number of randomly generated test problems. Statistical analyses based on the effect of each algorithm on each objective space show that the proposed MOPSO outperforms the CMOPSO in terms of quality, diversity and spacing metrics.

Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory

A novel prediction method based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using chaos theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.

Geometric particle swarm optimization for robust visual ego-motion estimation via particle filtering

Conventional particle filtering-based visual ego-motion estimation or visual odometry often suffers from large local linearization errors in the case of abrupt camera motion. The main contribution of this paper is to present a novel particle filtering-based visual ego-motion estimation algorithm that is especially robust to the abrupt camera motion. The robustness to the abrupt camera motion is achieved by multi-layered importance sampling via particle swarm optimization (PSO), which iteratively moves particles to higher likelihood region without local linearization of the measurement equation. Furthermore, we make the proposed visual ego-motion estimation algorithm in real-time by reformulating the conventional vector space PSO algorithm in consideration of the geometry of the special Euclidean group SE(3), which is a Lie group representing the space of 3-D camera poses. The performance of our proposed algorithm is experimentally evaluated and compared with the local linearization and unscented particle filter-based visual ego-motion estimation algorithms on both simulated and real data sets.

Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm

Melt index (MI) is considered as one of the most important variables of the quality, which determines the product specifications. Thus, a reliable estimation of MI is crucial in the quality control of the practical processes in the propylene polymerization (PP). An optimal soft sensor, named the least squares support vector machines with Ant Colony-Immune Clone Particle Swarm Optimization (AC-ICPSO-LSSVM), is therefore proposed. It combines the advantages of the high accuracy of LSSVM and the fast convergence of PSO. Furthermore, the immune clone (IC) method is introduced into the PSO algorithm to make the particles of ICPSO diverse and enhance global search capability for avoiding the premature convergence and local optimization of the conventional PSO algorithm. Besides, to widen data range, improve search precision and convergence efficiency, and avoid premature convergence, Ant Colony Optimization (ACO) is introduced to find the initial particles for PSO algorithm. The resultant hybrid AC-ICPSO algorithm is then applied to optimize the parameters of LSSVM, so the optimal prediction model of melt index, AC-ICPSO-LSSVM, is obtained. As the comparative basis, the models of ICPSO-LSSVM, PSO-LSSVM, and LSSVM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. These models are also compared with RBF method reported in the open literature. The research results reveal the prediction accuracy and validity of the proposed approach.

TWO-DIMENSIONAL PATTERN SYNTHESIS OF STACKED CONCENTRIC CIRCULAR ANTENNA ARRAYS USING BEE COLONY ALGORITHMS

Stacked concentric circular antenna arrays (SCCAA's) supporting both the scanning mode and the tracking mode are optimized in both the azimuth and elevation planes. The gbest- guided artiflcial bee colony algorithm (GABCA) is adopted to optimize the dual-mode fleld patterns of thinned SCCAA's. Performance comparison of the GABCA with conventional ABCA and particle swarm optimization (PSO) algorithms is also presented.

An Improved PSO Algorithm and Its Application to UWB Antenna Design

An improved particle swarm optimization (PSO) algorithm with a neighborhood-redispatch (NR) technique is presented to design an ultrawideband (UWB) antenna in this letter. Based on the conventional version of PSO algorithm, some new parameters are introduced in the NR-PSO algorithm to improve optimization performance. In order to illustrate the effectiveness of the algorithm in representative topology problems, several benchmark functions are used to test the performance of the improved PSO algorithm. The results show that it is better than the conventional PSO algorithm for multimodal and unimodal functions. Combined with the HFSS solver, one UWB antenna with a respective notched band is designed by using the proposed NR-PSO algorithm. It not only covers the UWB band (3.1-10.6 GHz) and Bluetooth passband (2.40-2.484 GHz), but also realizes a stopband (5.15-5.825 GHz) to avoid potential interferences.

Dynamic search space squeezing strategy based intelligent algorithm solutions to economic dispatch with multiple fuels

This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA_MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results.

Quantum-infusion 메커니즘을 이용한 분산형 입자군집최적화 알고리즘에 관한 연구

본 논문에서는 종래의 PSO 알고리즘 성능저하의 주요 원인들 중 하나인 입자들의 조기수렴 현상을 개선한 DPSO-QI (Distributed PSO with quantum-infusion mechanism) 기법을 제안한다. DPSO-QI 알고리즘은 다음과 같은 두 가지 특징을 지닌다. 첫째, 분산형 구조의 PSO 기법을 도입한다. 이는 먼저 적절한 수의 입자들로 소그룹을 형성하고, 최적해 탐색에 필요한 다양한 정보의 교환이 각 소그룹 내에서만 이루어지도록 한 기법이다. 이러한 기법을 바탕으로 입자들의 탐색 다양성을 증대시킴으로서 조기수렴 현상을 감소시키는 효과를 달성할 수 있다. 둘째, 상기의 입자 소그룹에 Quantum-infusion (QI) 메커니즘에 기반 한 기법을 도입시킨다. 이를 통해 입자들의 전역 최적해 탐색 정밀도를 보다 향상시킬 수 있다. 끝으로 다양한 수치예제를 통하여 제안하는 새로운 PSO 기법이 종래의 방식들에 비해 매우 뛰어난 성능을 구현할 수 있음을 입증하고자 한다. In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm.

A Modified PSO to Optimize Manufacturers Production and Delivery

This paper presents a new approach to the solution of optimal Manufacturers Production and Delivery (MPD) scheduling problem, using improved particle swarm optimization (ISAPSO) technique. The producing and delivery system is highly complex and possesses nonlinear relationship of the problem variables, products storage, products transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved particle swarm optimization (PSO) technique is suggested that deals with an inequality constraint treatment mechanism to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed ISAPSO is applied on the quick response (QR) manufacturing supply chain system using making to order (MTO) strategy. Numerical results are compared with those obtained by PSO and ISAPSO approaches. The simulation results reveal that the proposed ISAPSO appears to be the best in terms of convergence speed, solution time and minimum cost to the MPD problems.

Time-varying acceleration coefficients IPSO for solving dynamic economic dispatch with non-smooth cost function

The objective of the dynamic economic dispatch (DED) problem is to schedule power generation for the online units for a given time horizon economically, satisfying various operational constraints. Due to the effect of valve-point effects and prohibited operating zones (POZs) in the generating units cost functions, DED problem is a highly non-linear and non-convex optimization problem. The DED problem even may be more complicated if transmission losses and ramp-rate constraints are taken into account. This paper presents a novel and heuristic algorithm to solve DED problem of generating units, by employing time varying acceleration coefficients iteration particle swarm optimization (TVAC-IPSO) method. The effectiveness of the proposed method is examined and validated by carrying out extensive tests on different test systems, i.e. 5-unit and 10-unit test systems. Valve-point effects, POZs and ramp-rate constraints along with transmission losses are considered. To examine the efficiency of the proposed TVAC-IPSO algorithm, comprehensive studies are carried out, which compare convergence properties of the proposed TVAC-IPSO approach with conventional PSO algorithm, in addition to the other recently reported approaches. Numerical results show that the TVAC-IPSO method has good convergence properties and the generation costs resulted from the proposed method are lower than other algorithms reported in recent literature.

Fast Optimization of Electromagnetic Design Problems Using the Covariance Matrix Adaptation Evolutionary Strategy

A new method of optimization recently made popular in the evolutionary computation (EC) community is introduced and applied to several electromagnetics design problems. First, a functional overview of the covariance matrix adaptation evolutionary strategy (CMA-ES) is provided. Then, CMA-ES is critiqued alongside a conventional particle swarm optimization (PSO) algorithm via the design of a wideband stacked-patch antenna. Finally, the two algorithms are employed for the design of small to moderate size aperiodic ultrawideband antenna array layouts (up to 100 elements). The results of the two electromagnetics design problems illustrate the ability of CMA-ES to provide a robust, fast and user-friendly alternative to more conventional optimization strategies such as PSO. Moreover, the ultrawideband array designs that were created using CMA-ES are seen to exhibit performances surpassing the best examples that have been reported in recent literature.

A Continuous Ant Colony (C-ANT) algorithm solving the Economic Load Dispatch (ELD) Problem

Classic algorithms that are based on the ant system theory have been designed to face problems with discrete solutions. When dealing with non discrete problems, in order to apply Ant Colony Optimization (AGO) algorithms they should be transformed to discrete problems. The arbitrary limitation of the number of possible solutions for each space is the result of the transformation of the solutions of the problems from the continuous to the discrete workspace. Hence, the choice of the width of the solutions spaces essentially defines the possible best solutions of the problem. In order to deal with this disadvantage, we present a new algorithm: the Continuous Ant Colony (C-ANT). This algorithm encourages local searching around the best solution found in each iteration. The proposed (C-ANT) is applied to a simple ELD problem composed of 4 generators. Comparison to conventional Particle Swarm Optimization (PSO) algorithms is presented.

Optimal Location of FACTS Devices Using Adaptive Particle Swarm Optimization Hybrid with Simulated Annealing

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

Particle Swarm Optimization Algorithms Inspired by Immunity-Clonal Mechanism and Their Applications to Spam Detection

Compared to conventional PSO algorithm, particle swarm optimization algorithms inspired by immunity-clonal strategies are presented for their rapid convergence, easy implementation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization (CPSO) algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive generations, the CPSO enlarges the area near the promising candidate solution and accelerates the evolution of the swarm, leading to better optimization capability and faster convergence performance than conventional PSO. As a variant, an advance-and-retreat strategy is incorporated to find the nearby minima in an enlarged solution space for greatly accelerating the CPSO before the next clonal operation. A black hole model is also established for easy implementation and good performance. Detailed descriptions of the CPSO algorithm and its variants are elaborated. Extensive experiments on 15 benchmark test functions demonstrate that the proposed CPSO algorithms speedup the evolution procedure and improve the global optimization performance. Finally, an application of the proposed PSO algorithms to spam detection is provided in comparison with the other three methods.