• 대한산업공학회지
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• 제37권3호
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• pp.163-170
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• 2011

This paper proposes a modified version of Particle Swarm Optimization (PSO) called Information Diffusion PSO (ID-PSO). In PSO algorithms, premature convergence of particles could be prevented by defining proper population topology. In this paper, we propose a variant of PSO algorithm using a new population topology. We draw inspiration from the theory of information diffusion which models the transmission of information or a rumor as one-to-one interactions between people. In ID-PSO, a particle interacts with only one particle at each iteration and they share their personal best solutions and recognized best solutions. Each particle recognizes the best solution that it has experienced or has learned from another particle as the recognized best. Computational experiments on the benchmark functions show the effectiveness of the proposed algorithm compared with the existing methods which use different population topologies.

• Journal of Electrical Engineering and Technology
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• 제5권3호
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• pp.423-430
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• 2010

Particle swarm optimization (PSO) algorithm is designed to find a single global optimal point. However, the PSO needs to be modified in order to find multiple optimal points of a multimodal function. These modifications usually divide a swarm of particles into multiple subswarms; in turn, these subswarms try to find their own optimal point, resulting in multiple optimal points. In this work, we present a new PSO algorithm, called coupling PSO to find multiple optimal points of a multimodal function based on coupling particles. In the coupling PSO, each main particle may generate a new particle to form a couple, after which the couple searches its own optimal point using non-stop-moving PSO algorithm. We tested the suggested algorithm and other ones, such as clustering PSO and niche PSO, over three analytic functions. The coupling PSO algorithm was also applied to solve a significant benchmark problem, the TEAM workshop problem 22.

• 정보처리학회논문지B
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• 제16B권4호
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization)는 군집(swarm)을 구성하는 단순한 개체들인 입자(particle)들이 각자의 경험을 공유하여 문제의 해답을 찾는 최적화 알고리즘으로 다양한 분야에서 응용되고 있다. PSO에 대한 연구는 최적화를 위해 군집이 적합한 영역으로 빠르게 수렴하도록 하는 파라미터 값의 선정, 토폴로지, 입자의 이동에서 주로 이루어지고 있다. 표준 PSO 알고리즘은 입자 자신과 최고의 이웃이 제공하는 정보만을 이용해서 이동하므로 다양한 영역을 탐색하지 못하고 지역적 최적점에 조기 수렴하는 경향이 있다. 본 논문에서는 군집이 다양한 영역을 탐색하기 위해, 각 입자는 더 나은 경험을 가진 이웃입자들의 정보를 상대적인 중요도에 따라서 참조하여 이동하도록 하였다. 다양한 영역의 탐색은 표준 PSO 알고리즘보다 지역적 최적화의 확률을 줄이고 탐색 속도를 가속화하며 탐색의 성공률을 높일 수 있다. 또한 군집이 지역적 미니멈으로부터 벗어나기 위한 검사 전략을 제안하여 탐색의 성공률을 높였다. 제안한 PSO 알고리즘을 평가하기 위하여, 벤치마크 함수들에 적용한 결과 최적화의 진행 속도 개선과 탐색 성공률의 향상이 있었다.

• Structural Engineering and Mechanics
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• 제54권3호
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• pp.453-474
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• 2015

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

• 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.

• 산업경영시스템학회지
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• 제33권4호
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• Structural Engineering and Mechanics
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• 제52권3호
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• pp.445-468
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• 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.

• 전기전자학회논문지
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• 제22권2호
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• pp.455-459
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• 2018

In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.