Vector Evaluated Particle Swarm Optimization Research Articles

A Quick Search Dynamic Vector-Evaluated Particle Swarm Optimization Algorithm Based on Fitness Distance

A quick search dynamic vector-evaluated particle swarm optimization algorithm based on fitness distance (DVEPSO/FD) is proposed according to the fact that some dynamic multi-objective optimization methods, such as the DVEPSO, cannot achieve a very accurate Pareto optimal front (POF) tracked after each objective changes, although they exhibit advantages in multi-objective optimization. Featuring a repository update mechanism using the fitness distance together with a quick search mechanism, the DVEPSO/FD is capable of obtaining the optimal values that are closer to the real POF. The fitness distance is used to streamline the repository to improve the distribution of nondominant solutions, and the flight parameters of the particles are adjusted dynamically to improve the search speed. Groups of the standard benchmark experiments are conducted and the results show that, compared with the DVEPSO method, from the figures generated by the test functions, DVEPSO/FD achieves a higher accuracy and clearness with the POF dynamically changing; from the values of performance indexes, the DVEPSO/FD effectively improves the accuracy of the tracked POF without destroying the stability. The proposed DVEPSO/FD method shows a good dynamic change adaptability and solving set ability of the dynamic multi-objective optimization problem.

An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.

Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms

A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

Vector evaluated particle swarm optimisation for multi-objective structural system identification

This paper presents the application of VEPSO - a cross searching multi-objective technique - to an inverse problem for identifying structural parameters and damage detection from time domain responses. The aim here is to minimise both instantaneous vibrational power flow balance and acceleration deviations. Whereas minimisation of acceleration deviations is fairly conventional, minimising the additional parameter of power flow balance is a novel concept used with VEPSO here. Instantaneous substructural power flows are balanced by equating the input power to the dissipated and damping powers and the time rate of change of kinetic and strain energies. Numerical simulations are performed for substructures taken from a lumped mass system for parameter identification and a cantilever beam for structural identification and multiple crack damage detection. In these numerical simulations, noise free and noise contaminated response measurements are considered. The results demonstrate that the VEPSO method can be accurately used for identifying the structural parameters and detecting damages of a system.

In-cluster vector evaluated particle swarm optimization for distributed regression in WSNs

Conventional methods address data modeling in WSNs by converting a learning problem into an optimization task. These methods are mainly based on Gradient Descent and Nelder–Mead Simplex optimization techniques. They are energy efficient, but with drawbacks: the accuracy of the induced models is usually lower than that of the centralized technique; and the latency is proportional to the network size. In this paper, we propose a novel distributed method for parametric regression in a clustered WSN using particle swarm optimization. Two main contributions of this paper are as follows: First, we re-formulate the distributed regression in WSNs as a multiobjective optimization (MO) problem in which an objective is dedicated to each cluster. Second, we propose a distributed algorithm based on the Vector Evaluated Particle Swarm Optimization method to address the MO problem in two phases. The proposed algorithm obtains a set of candidate network regressors and computes the final model using a weighted averaging rule. We compare the prediction accuracy, latency, and energy consumption of our algorithm against its popular distributed counterparts and the centralized technique using a real-world and an artificial data set. The experimental results show that our algorithm outperforms the existing approaches in prediction accuracy while its energy consumption and latency are acceptable. We also evaluate the effect of the number of particles, swarm topologies, and clustering granularity on the performance of the proposed method.

Improving vector evaluated particle swarm optimisation using multiple nondominated leaders.

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms.

2C33 Motion Control and Optimization of a Ball Throwing Robot with a Flexible Arm(The 12th International Conference on Motion and Vibration Control)

Motion control of flexible arms is more difficult than that of rigid arms. However utilizing its dynamics enables improved performance such as a fast motion in short operation time. This paper investigates a ball throwing robot with one flexible link. This robot throws a ball at a set speed. A mathematical model of this ball throwing robot is derived through Hamilton's principle. The sinusoidal patterns of torque input and structure are designed through the proposed simulation models. The parameters of each torque input pattern and flexible structure are optimized and determined by chaos embedded vector evaluated particle swarm optimization (CEVEPSO). Finally, the residual vibration of the manipulator after throwing is suppressed with input shaping technique.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Swarm Intelligence and Its Applications

Swarm Intelligence and Its Applications

Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System

This paper addresses a robust method for optimal PID control of Automatic Voltage Regulation (AVR) system to damp terminal voltage oscillation following disturbances in power systems. The optimization is carried out by the Anar- chic Society Optimization (ASO) algorithm which is inspired by a social grouping in which members behave anarchically to improve their situation. The optimal tuning problem of the PID gains to control of AVR system against parametric uncer- tainties is formulated as an optimization problem according to time domain based objective function. It is solved by the ASO algorithm which has a strong ability to find the most optimistic results to reduce control system effort and find optimal design. It is carried out under multiple operation conditions to achieve the desired level of terminal voltage regulation. The effec- tiveness of the optimized PID controller with ACO algorithm is investigated under system parameter changes in comparison with the results of PID controller tuned via of the recently reported optimization method like the craziness based Particle Swarm Optimization (PSO) and Vector Evaluated PSO (VEPSO) approaches. The simulation results is depicted to illustrate robust performance of the proposed ASO technique for enhancement transient stability of AVR system that of the other heuristics methods in the literature. The results analysis reveals that the ASO based PID type AVR control system is effective and provides good terminal voltage oscillations damping ability. Moreover, it is superior that of the other PSO method in terms of accuracy, convergence and computational effort.

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem.

A bi-objective location area planning for wireless phone network

In this paper, we propose an extension of the classical location area planning (LAP) problem in the wireless telephony system including additional objectives and constraints. The bi-objective location area planning (BOLAP) problem considers the minimisation of both distance costs between base transceiver stations and the base station controllers. A vector evaluated particle swarm optimisation (VEPSO) is then implemented to solve the bi-objective model. This heuristic operates in term of two swarms, each corresponding to one of the two objectives, and exchanging their best experience. A comparison of the proposed algorithm is performed with the statistical method being used by a major wireless telephone company. The empirical results show that the proposed approach dominates the operator’s method on one hand, and that the BOLAP is an effective generalisation of the classical LAP model on the other hand.

Minimum energy trajectory planning for vibration control of a flexible manipulator using a multi-objective optimisation approach

In this study, we deal with a point-to-point motion task for a flexible manipulator and develop a minimum energy trajectory planning method for residual vibration suppression, in which soft computing techniques are used. An artificial neural network (ANN) is employed to generate the joint angle of the manipulator. For the ANN, we use a vector evaluated particle swarm optimisation (VEPSO) algorithm as the learning algorithm. The maximum residual vibration amplitude and the operating energy are adopted as multi-objective functions of the VEPSO algorithm. By operating the manipulator along the trajectory thus generated, the suppression of the residual vibration can be realised with minimum consumption of driving energy. In other words, the proposed method is an open-loop control that does not require sensors to measure unwanted vibrations. The performance of the proposed control scheme is confirmed by numerical simulation. In addition, the effectiveness of the proposed approach is experimentally verified.

An Approach to Frequent Pattern Discovery from Gene Expression Data Using PSO Variants

Pattern mining has always attracted a huge attention for generation of large amount of patterns and association between them. Though it's one of the major data mining tasks but it has always been a time consuming process as a large scale of patterns and associations rules gets generated. To reduce the time of consumption it was preferable to discretize the data matrix in the range of 0 to 1 and for this the fuzzy the membership function has been used which is quite simple in its concept and strategy. Owing to the concept of fuzzy logic, certain evolutionary algorithms (EAs) also gained popularity to optimize the process of mining patterns from the fuzzy sets. For this, Particle swarm optimization (PSO) was used which is supposed to provide better results as compared to other EA like genetic algorithm, ant colony optimization etc. But it was found that there are certain versions of PSO that provided much better results than the standard PSO algorithm. In this paper, the gene expression data set was fuzzified for the purpose of discretization in the range of 0 to 1. A Frequent Pattern (FP) growth algorithm was used to generate set of frequent patterns. These patterns were used as the initial population and the mean squared residue (MSR) score was used as an evaluation criteria. Fully Informed Particle Swarm Optimization (FIPSO), Dynamic Multi Swarm Particle Swarm Optimization (DMS-PSO), Comprehensive Learning Particle Swarm Optimization (CLPSO), Vector Evaluated Particle Swarm Optimization (VEPSO) etc are the certain versions of PSO that were used and they provided much better results as compared to standard PSO algorithm. But the VEPSO algorithm outperformed the other three algorithms in terms of generation of best individual frequent patterns, runtime and the volume of mean squared residue (lower the MSR score the better is the quality of the patterns).

138 2自由度フレキシブルマニピュレータの省エネルギーフィードフォワード制御法

This paper investigates a feedforward control technique for saving the operating energy of a 2-DOF flexible manipulator with a point-to-point (FTP) motion, in which the residual vibration also can be suppressed. The 2-DOF manipulator has one prismatic joint and one revolute joint. In order to obtain a mathematical model properly, the flexible manipulator is modeled by considering the axial displacement due to large bending deformation. The Lagrangian approach in conjunction with the assumed modes method is applied to derive the equations of motion of the manipulator system. For the FTP motion task, the trajectory of the translational motion is set to a cycloidal motion. On the other hand, the trajectory of the rotational motion is designed to simultaneously minimize the residual vibration and the operating energy. In the present method, we attempt to express the trajectory of the joint angle by an artificial neural network (ANN), and then a vector evaluated particle swarm optimization (VEPSO) algorithm, which is a multi-objective optimization algorithm, is used for learning the ANN. By operating the manipulator along the trajectory obtained by the proposed method, the residual vibrations can be suppressed under the minimum energy condition. The numerical simulation results are compared with the experimental results; this comparison reveals the applicability and effectiveness of the proposed method.

Nature inspired optimization techniques for the design optimization of laminated composite structures using failure criteria

The design optimization of laminated composites using naturally inspired optimization techniques such as vector evaluated particle swarm optimization ( VEPSO) and genetic algorithms ( GA) are used in this paper. The design optimization of minimum weight of the laminated composite is evaluated using different failure criteria. The failure criteria considered are maximum stress ( MS), Tsai–Wu ( TW) and failure mechanism based ( FMB) failure criteria. Minimum weight of the laminates are obtained for different failure criteria using VEPSO and GA for different combinations of loading. From the study it is evident that VEPSO and GA predict almost the same minimum weight of the laminate for the given loading. Comparison of minimum weight of the laminates by different failure criteria differ for some loading combinations. The comparison shows that FMBFC provide better results for all combinations of loading.

Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particular entrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are – the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai–Wu failure criteria. The optimization method is validated for a number of different loading configurations – uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented.

Determining Generator Contributions to Transmission System Using Parallel Vector Evaluated Particle Swarm Optimization

In this paper, the generator contributions to the transmission system are determined by an evolutionary computation technique. Evaluating the contributions of generators to the power flows in transmission lines is formulated as a multiobjective optimization problem and calculated using a parallel vector evaluated particle swarm optimization (VEPSO) algorithm. Specifically, the contributions are modeled by particles of swarms whose positions are optimally determined while satisfying all multiobjectives and other physical and operating constraints. The VEPSO method is parallelized by distributing the swarms in a number of networked PCs. The proposed parallel VEPSO algorithm accounts for nonlinear characteristics of the generators and transmission lines. The applicability of the proposed parallel VEPSO algorithm in accessing the generator contributions is demonstrated and compared with analytical methods for four different systems: three-bus, six-bus, IEEE 30-bus, and 136-bus test systems. The experimental results show that the proposed parallel VEPSO algorithm is capable of obtaining precise solutions compared to analytical methods while considering nonlinear characteristics of the systems.