Vector Evaluated Particle Swarm Optimization Algorithm 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.

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).

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.