Differential Evolution Particle Swarm Optimization Research Articles

Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization

Differential evolution (DE), a simple evolutionary algorithm which shows superior performance in global optimization. Since it utilizes the differential information to get the new candidate solution, sometimes it results in instability of performance. Particle swarm optimization (PSO) is widely used to solve the optimization problems as it can converge quickly. But PSO easily gets stuck in local optima. Hybridization of DE and PSO (DEPSO) eliminates the disadvantages of both. This paper presents the application of DEPSO algorithm to determine the maximum loadability limit of power system. It is tested on Matpower 30 bus and IEEE 118 bus systems. To compare the performance of this DEPSO algorithm with other evolutionary algorithms like DE and Multi Agent Hybrid PSO, statistical measures like best, mean, standard deviation of results and average computation time over 20 independent trials are considered here. The results show the better performance of DEPSO algorithm to solve the maximum loadability problem. DEPSO algorithm provides high maximum loading point in reduced time.

A Comparison Study of Validity Indices on Swarm-Intelligence-Based Clustering

Swarm intelligence has emerged as a worthwhile class of clustering methods due to its convenient implementation, parallel capability, ability to avoid local minima, and other advantages. In such applications, clustering validity indices usually operate as fitness functions to evaluate the qualities of the obtained clusters. However, as the validity indices are usually data dependent and are designed to address certain types of data, the selection of different indices as the fitness functions may critically affect cluster quality. Here, we compare the performances of eight well-known and widely used clustering validity indices, namely, the Caliński-Harabasz index, the CS index, the Davies-Bouldin index, the Dunn index with two of its generalized versions, the I index, and the silhouette statistic index, on both synthetic and real data sets in the framework of differential-evolution-particle-swarm-optimization (DEPSO)-based clustering. DEPSO is a hybrid evolutionary algorithm of the stochastic optimization approach (differential evolution) and the swarm intelligence method (particle swarm optimization) that further increases the search capability and achieves higher flexibility in exploring the problem space. According to the experimental results, we find that the silhouette statistic index stands out in most of the data sets that we examined. Meanwhile, we suggest that users reach their conclusions not just based on only one index, but after considering the results of several indices to achieve reliable clustering structures.

Optimisation of Tubular Hydroforming Processes for Wrinkling and Thinning Control

This paper is focused on the development and implementation of an innovative optimisation algorithm for the wrinkling and thinning control during the hydroforming of complex metallic parts. A straightforward numerical algorithm for simultaneous tracking and evaluation of the initiation of wrinkling/thinning defects was implemented, together with a numerical simulation program based on the Finite Element Method (FEM). After undesirable wrinkling/thinning patterns are identified during FEM simulation, the developed optimisation procedure (called Hybrid Differential Evolution Particle Swarm Optimisation - HDEPSO) is responsible to automatically correct the process input parameters, in order to achieve the successful forming of the desired part. In the end, the combined procedure (optimisation methodology + FEM) proved to be able to lead to a suitable numerical simulation and design tool for industrial hydroformed metallic tubular parts.

A reliable method of minimum zone evaluation of cylindricity and conicity from coordinate measurement data

The form error evaluation of cylinders and cones is very important in precision coordinate metrology. The solution of the traditional least squares technique is prone to over-estimation, as a result unnecessary rejections may be caused. This paper proposes a reliable algorithm to calculate the minimum zone form errors of cylinders and cones, called a hybrid particle swarm optimization-differential evolution algorithm. The optimization is conducted in two stages, so that the program can hold a fast convergence rate, while effectively avoiding local minima. Experimental results demonstrate that the proposed algorithm can obtain very accurate and stable results for the calculation of cylindricity and conicity.

Elasto-plastic buckling of integrally stiffened panels (ISP): An optimization approach for the design of cross-section profiles

In this work, three optimization algorithms—Levenberg–Marquardt, simulated annealing and a newly developed and proposed hybrid differential evolution particle swarm optimization—are considered in the shape optimizations of cross-sections of integrally stiffened panels (ISP) for aeronautical applications, when subjected to buckling deformation modes within the elasto-plastic range. The proposed algorithm is shown to be robust and more effective, in the tested examples, than conventional optimization algorithms, leading to optimum designs of ISP cross-sections for a pre-defined buckling load-carrying capacity, combined with elasto-plastic nonlinear behaviors.

Hybrid Fuzzy Adaptive Particle Swarm Optimization and Differential Evolution Algorithm for Distribution Feeder Reconfiguration

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.

Particle swarm optimization with quantum infusion for system identification

System identification is a challenging and complex optimization problem due to nonlinearity of the systems and even more in a dynamic environment. Adaptive infinite impulse response (IIR) systems are preferably used in modeling real world systems because of their reduced number of coefficients and better performance over the finite impulse response filters. Particle swarm optimization (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimization problems. In this paper, PSO with quantum infusion (PSO–QI) is used in identification of benchmark IIR systems and a real world problem in power systems. PSO–QI’s performance is compared with PSO and differential evolution PSO (DEPSO) algorithms. The results show that PSO–QI has better performance over these algorithms in identifying dynamical systems.

Maximum Loading Enhancement by Differential Evolution Particle Swarm Optimization Based Optimal Power Flow Solution

Maximum Loading Enhancement by Differential Evolution Particle Swarm Optimization Based Optimal Power Flow Solution

An integrated method of particle swarm optimization and differential evolution

Particle swarm optimization (PSO) and differential evolution (DE) have their similarities and compatibility in the design update process, such that a new design vector is determined by using neighborhood designs under algorithm control parameters. The paper deals with an integrated method of a hybrid PSO (HPSO) algorithm combined with DE in order to refine the optimization performance. PSO and DE also possess common characteristics compared with genetic algorithm (GA). The crossover- and mutation-like operators are suggested in the HPSO. A bounce back method is also implemented to prevent the design from locating out of design spaces during the optimization process. For the purpose of further enhancing the search capabilities, such HPSO is combined with the Q-learning that is one of efficient reinforcement learning methods. Using a number of nonlinear multimodal functions and engineering optimization problems, the proposed algorithms of HPSO and HPSO with Q-learning are compared with PSO DE and GA.

New and Accurate Synthesis Formulas for Asymmetric Coplanar Stripline with an Infinitely Wide Strip

New and accurate synthesis formulas for the asymmetric coplanar stripline with an infinitely wide strip (ACPS-IWS) are presented. They are obtained by using a differential evolution algorithm (DEA) and particle swarm optimization (PSO) algorithm, and are useful to microwave engineers for accurately computing the physical dimensions of ACPS-IWS. The average percentage errors of the synthesis formulas found by using DEA and PSO are calculated to be 0.85% and 1.62%, respectively, for 4913 ACPS-IWS samples having different electrical parameters and physical dimensions, as compared with the results of quasi-static analysis.

EVOLVING DIGITAL CIRCUITS USING HYBRID PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.