• International Journal of Control, Automation, and Systems
• /
• v.6 no.4
• /
• pp.488-494
• /
• 2008

This paper proposes an effective approach based on binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration. The proposed method considers the advantages and disadvantages of existing particle swarm optimization method and redefined the operators of PSO algorithm to fit the application field of distribution systems. Shift operator is proposed to construct the binary coding particle swarm optimization for feeder reconfiguration. A typical distribution system of Taiwan Power Company is used in this paper to demonstrate the effectiveness of the proposed method. The test results show that the proposed method can apply to feeder reconfiguration problems more effectively and stably than existing method.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.9
• /
• pp.884-891
• /
• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.2
• /
• pp.182-191
• /
• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• Journal of Distribution Science
• /
• v.11 no.3
• /
• pp.5-11
• /
• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.4
• /
• pp.1329-1339
• /
• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• Structural Engineering and Mechanics
• /
• v.52 no.3
• /
• pp.445-468
• /
• 2014

In this paper a new particle swarm ray optimization algorithm is proposed for truss shape and size optimization with natural frequency constraints. These problems are believed to represent nonlinear and non-convex search spaces with several local optima and therefore are suitable for examining the capabilities of new algorithms. The proposed algorithm can be viewed as a hybridization of Particle Swarm Optimization (PSO) and the recently proposed Ray Optimization (RO) algorithms. In fact the exploration capabilities of the PSO are tried to be promoted using some concepts of the RO. Five numerical examples are examined in order to inspect the viability of the proposed algorithm. The results are compared with those of the PSO and some other existing algorithms. It is shown that the proposed algorithm obtains lighter structures in comparison to other methods most of the time. As will be discussed, the algorithm's performance can be attributed to its appropriate exploration/exploitation balance.

• Journal of the Korea Institute of Building Construction
• /
• v.17 no.4
• /
• pp.369-375
• /
• 2017

This study suggests PSO(Particle Swarm Optimization) algorithm that optimizes the snowplow route allocation and the location of the snow control material storage to improve the efficiency in snow removal works. The modified PSO algorithm for improving the search capacity is proposed, and this study suggests the solution representation, the parameter setting, and the fitness function for the given optimization problems. Computational experiments in real-world case are carried out to justify the proposed method and compared with the traditional PSO algorithms. The results show that the proposed algorithms can find the better solution than the traditional PSO algorithms by searching for the wider solution space without falling into the local optima. The finding of this study is efficiently employed to solve the optimization of the snowplow route allocation by minimizing the workload of each snowplow to search the location of the snow control material storage as well.

• Proceedings of the KIEE Conference
• /
• 2006.11a
• /
• pp.198-200
• /
• 2006

This paper presents a development of an educational simulator of particle swarm optimization (PSO) and application for solving the test functions and economic dispatch (ED) problems with nonsmooth cost functions. A particle swarm optimization is one of the most powerful methods for solving global optimization problems. It is a population-based search algorithm and searches in parallel using a group of particles similar to other AI-based heuristic optimization techniques. In developed simulator, lecturers and students can select the functions for simulation and set the parameters that have an influence on PSO performance. To improve searching capability for ED problems, a crossover operation is proposed to the position update of each individual (CR-PSO). To verify the feasibility of CR-PSO method, numerical studies have been performed for two different sample systems. The proposed CR-PSO method outperforms other algorithms in solving ED problems.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.1166-1169
• /
• 2012

본 논문은 사용자 행동인식을 위해 기존 PSO (Particle Swarm Optimization) 알고리즘의 경계선을 통한 데이터 분류에서 데이터의 수집환경에 의해 발생하는 문제를 벡터의 길이비교를 이용한 보정을 통해 보완한 알고리즘을 제안한다. 기존의 PSO 알고리즘은 데이터 분류를 위해서 데이터의 최소, 최대값을 이용하여 경계를 생성하고, 이를 이용하여 데이터를 분류하였다. 그러나 PSO를 이용하여 행동인식을 할 때 행동이 수집되는 환경에 따라서 경계에 포함되지 못해 행동이 분류되지 못하는 문제가 있다. 이러한 분류의 문제를 보완하기 위해 경계를 벗어난 데이터와 각 행동을 대표하는 데이터의 벡터 길이를 계산하고 최소길이를 비교하여 분류한다. 실험결과, 기존 PSO 방법에 비해 개선된 방법이 평균적으로 앉기 1%, 걷기 7%, 서기 7%의 개선된 결과를 얻었다.

• Geophysics and Geophysical Exploration
• /
• v.22 no.4
• /
• pp.186-194
• /
• 2019

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.