• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.15-26
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• 2014

This paper proposes a Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called 'rbest' is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.185-193
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• 2009

This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1297-1302
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• 2014

This paper presents modern optimization methods for determining the optimal parameters of proportional-integral-derivative (PID) controller for coupled tank systems. The main objective is to obtain a fast and stable control system for coupled tank systems by tuning of the PID controller using the Particle Swarm Optimization algorithm. The result is compared in terms of system transient characteristics in time domain. The obtained results using the Particle Swarm Optimization algorithm are also compared to conventional PID tuning method like the Ziegler-Nichols tuning method, the Cohen-Coon method and IMC (Internal Model Control). The simulation results have been simulated by MATLAB and show that tuning the PID controller using the Particle Swarm Optimization (PSO) algorithm provides a fast and stable control system with low overshoot, fast rise time and settling time.

• Structural Engineering and Mechanics
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• 제47권5호
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• pp.713-727
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• 2013

In current study, natural frequency response of fiber metal laminated (FML) fibrous composite panels is optimized under different combination of the three classical boundary conditions using particle swarm optimization (PSO) algorithm and finite strip method (FSM). The ply angles, numbers of layers, panel length/width ratios, edge conditions and thickness of metal sheets are chosen as design variables. The formulation of the panel is based on the classical laminated plate theory (CLPT), and numerical results are obtained by the semi-analytical finite strip method. The superiority of the PSO algorithm is demonstrated by comparing with the simple genetic algorithm.

• 전기학회논문지
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• 제56권6호
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• pp.1023-1030
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• 2007

This paper presents a new and efficient approach for solving the economic dispatch (ED) problems with nonconvex cost functions using particle swarm optimization (PSO). Although the PSO is easy to implement and has been empirically shown to perform well on many optimization problems, it may easily get trapped in a local optimum when solving problems with multiple local optima and heavily constrained. This paper proposes an improved PSO, which combines the conventional PSO with chaotic sequences (CPSO). The chaotic sequences combined with the linearly decreasing inertia weights in PSO are devised to improve the global searching capability and escaping from local minimum. To verify the feasibility of the proposed method, numerical studies have been performed for two different nonconvex ED test systems and its results are compared with those of previous works. The proposed CPSO algorithm outperforms other state-of-the-art algorithms in solving ED problems, which consider valve-point and multi-fuels with valve-point effects.

• 한국산학기술학회논문지
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• 제14권5호
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• pp.2536-2543
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• 2013

본 논문에서는 단일존 빌딩의 모델링과 PSO 알고리즘을 이용한 냉방시스템 제어구간 건물 실내온도 제어 알고리즘을 제안한다. 최적제어를 하기 위한 제어구간 설정은 스위칭방법과 PSO 알고리즘을 사용하고 냉방시스템 사용요금은 TOU와 피크요금을 포함 하여 산정한다. 시뮬레이션을 통해 제안한 제어구간 설정방법을 적용하면 전력 사용에 따른 비용의 절감과 피크전력 절감을 확인할 수 있다.

• Industrial Engineering and Management Systems
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• 제11권3호
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• pp.215-223
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• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• 전기학회논문지
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• 제56권8호
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• pp.1382-1389
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• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• 한국정보전자통신기술학회논문지
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• 제15권5호
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• pp.367-379
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• 2022

본 논문에서는 입자 군집 최적화 (Particle Swarm Optimization: PSO) 알고리즘을 이용하여 다층 레이더 흡수 구조체를 설계하고, 다층 레이더 흡수 구조체의 특성을 분석하였다. 다층 레이더 흡수 구조체 설계에 PSO를 적용함으로써 빠르고 정확하게 설계 값을 도출할 수 있음을 보였으며, 특히 경사 입사에 대한 경우에 대해서도 최적의 다층 레이더 흡수 구조체를 설계할 수 있음을 보였다. 또한, 다양한 설계 파라미터의 조합에서도 성능 요구 조건에 부합하는 최적의 값이 결정될 수 있음을 보였다. 각 단계별로 필요한 방정식 및 모든 변수에 대한 자세한 설명을 포함해서 포괄적인 순서도를 통해 제시하였고 본 논문의 결과로부터 다층 레이더 흡수 구조체를 설계하기 위한 복잡하고 많은 계산을 생략할 수 있으며, 다양한 복합 재료를 활용한 다층 레이다 흡수 구조체 설계 및 개발에 활용할 수 있다.

• 제어로봇시스템학회논문지
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• 제20권8호
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• pp.854-867
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• 2014

This paper deals with the performance comparison of a PSO algorithm inspired in the process of simulating the behavior pattern of the organisms. The PSO algorithm finds the optimal solution (fitness value) of the objective function based on a stochastic process. Generally, the stochastic process, a random function, is used with the expression related to the velocity included in the PSO algorithm. In this case, the random function of the normal distribution (Gaussian) or uniform distribution are mainly used as the random function in a PSO algorithm. However, in this paper, because the probability distribution which is various with 2 shape parameters can be expressed, the performance comparison of a PSO algorithm using the beta probability distribution function, that is a random function which has a high degree of freedom, is introduced. For performance comparison, 3 functions (Rastrigin, Rosenbrock, Schwefel) were selected among the benchmark Set. And the convergence property was compared and analyzed using PSO-FIW to find the optimal solution.