• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• 한국정보통신학회논문지
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• 제13권7호
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• pp.1312-1320
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• 2009

본 논문은 Particle Swarm Optimization(PSO)을 이용하여 2차원 IIR 디지털필터의 설계방법을 제안하였다. 먼저 2차원 디지털필터의 설계문제를 PSO에 적용하기 위하여 최소화 문제로써 형식화 과정이 논의된다. 제안된 PSO 알고리즘을 이용한 설계방법은 기존의 PSO 알고리즘에 IIR 필터설계에서 요구되는 안정성을 보증하는 과정이 검토되어 개선된다. 본 논문에서 제안된 방법의 타당성을 설계예시를 통해 고찰한 결과, 설계된 디지털필터는 동일한 설계사양으로 기존의 설계방법으로 설계된 디지털필터보다 근사오차 면에서 우수한 결과를 얻을 수 있었다. 또한 제안한 설계방법에 의한 2차원 IIR 디지털필터는 설계과정에서 필터의 안정성을 보증할 수 있었다.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.566-571
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• 2008

본 논문에서는 Particle Swarm Optimization(PSO) 기법을 이용한 이족보행로봇의 보행 계획 방법을 제안한다. 이족보행로봇의 보행 프리미티브를 기반으로 PSO의 학습 및 군집 특성을 이용하여 장애물이 있는 2차원 작업공간에서 보행 계획 방법을 설계하였다. 먼저 PSO의 탐색알고리즘을 사용하여 장애물을 회피하는 실행 가능한 보행 프리미티브들의 순서를 찾아서 보행 경로를 생성하고, 탐색된 경로를 바탕으로 보행 걸음수와 이동 거리를 최적화 하는 경로 최적화 알고리즘을 제안하였다. 제안된 보행 계획방법은 다양한 구성의 장애물을 포함한 작업환경에서 모의실험을 통하여 발걸음 탐색 시간이 줄고 최적화된 보행 경로를 생성하는 것을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.786_787
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• 2009

This paper proposes a novel multimodal optimization method, Coupling particles swarm optimization (PSO), to find all optima in design space. This method based on the conventional Particle Swarm Optimization with modifications. The Coupling method is applied to make a couple from main particle and then each couple of particles searches its own optimum by using non-stop-moving PSO. We tested out our method and other one, such as ClusteringParticle Swarm Optimization and Niche Particle Swarm Optimization, on three analytic functions. The Coupling Particle Swarm Optimization is also applied to solve a significant benchmark problem, the TEAM workshop benchmark problem 22

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.272-274
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• 2006

In this paper, we concentrate on the mask design problem for optical micro-lithography. The pre-distorted mask is obtained by minimizing the error between the designed output image and the projected output image. We use the particle swarm optimization(PSO) and fuzzy system to insure that the resulting images are identical to the desired image. Our method has good performance for the iteration number by an experiment.

• Structural Engineering and Mechanics
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• 제42권3호
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.488-494
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• 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.

• Structural Engineering and Mechanics
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• 제70권5호
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Transactions on Electrical and Electronic Materials
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• 제18권3호
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• pp.129-135
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• 2017

This paper introduces a design and a simulation of a hybrid active power filter (HAPF) for harmonics reduction given an ideal supply source. The synchronous reference frame method has been used here to identify the reference currents. The proposed HAPF uses a new artificial- intelligence technique called Particle Swarm Optimization (PSO) for tuning the parameters of a proportional and integral controller called PI-PSO. The PI-PSO controller is used to archive optimality for the DC-link voltage of the HAPF-inverter. The hysteresis non-linear current control method is used in this approach to compare the extracted reference and the actual currents in order to generate the pulse gate required for the HAPF. Results obtained by simulations with Matlab/Simuling show that the proposed approach is very flexible and effective for eliminating harmonic currents generated by the non-linear load with the HAPF based PSO tuning.