• Journal of Digital Convergence
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• v.14 no.8
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• pp.293-303
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• 2016

Analysis of bargaining games utilizing evolutionary computation in recent years has dealt with important issues in the field of game theory. In this paper, we investigated interaction and coevolution process among heterogeneous artificial agents using evolutionary computation in the bargaining game. We present three kinds of evolving-strategic agents participating in the bargaining games; genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE). The co-evolutionary processes among three kinds of artificial agents which are GA-agent, PSO-agent, and DE-agent are tested to observe which EC-agent shows the best performance in the bargaining game. The simulation results show that a PSO-agent is better than a GA-agent and a DE-agent, and that a GA-agent is better than a DE-agent with respect to co-evolution in bargaining game. In order to understand why a PSO-agent is the best among three kinds of artificial agents in the bargaining game, we observed the strategies of artificial agents after completion of game. The results indicated that the PSO-agent evolves in direction of the strategy to gain as much as possible at the risk of gaining no property upon failure of the transaction, while the GA-agent and the DE-agent evolve in direction of the strategy to accomplish the transaction regardless of the quantity.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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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 the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.12-19
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• 2016

In the case of hard problems to find solutions or complx combination problems, there are various optimization algorithms that are used to solve the problem. Among these optimization algorithms, the representative of the optimization algorithm created by imitating the behavior patterns of the organism is the PSO (Particle Swarm Optimization) algorithm. Since the PSO algorithm is easily implemented, and has superior performance, the PSO algorithm has been used in many fields, and has been applied. In particular, PSO-SAPARB (PSO with Swarm Arrangement, Parameter Adjustment and Reflective Boundary) algorithm is an advanced PSO algorithm created to complement the shortcomings of PSO algorithm. In this paper, this PSO-SAPARB algorithm was applied to the longitudinal controller design of a VTOL (Vertical Take-Off and Landing) aircraft that has the advantages of fixed-wing aircraft and rotorcraft among drones which has attracted attention in the field of UAVs. Also, through the introduction and performance of the Korean SBAS (Satellite Based Augmentation System) named KASS (Korea Augmentation Satellite System) which is being developed currently, this paper deals with the availability of algorithm such as the PSO-SAPARB.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.8
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• pp.676-683
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• 2016

In this paper, an adaptive nulling algorithm which can be used to form nulls in the direction of jammer or interference signals in array antennas of single port system is proposed. The proposed adaptive algorithm does not require a priori knowledge of the incoming signal direction and can be applied to the partially adaptive arrays. This algorithm is the combination of the PSO(Particle Swam Optimization) algorithm and the gradient-based perturbation adaptive algorithm, which shows stable nulling performance adaptively even on the moving jammer environment where the incident direction of the interference signal is changing with time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1578-1583
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• 2013

In this paper, stochastic optimization algorithms of PSO (Particle Swarm Optimization) and APSO (Adaptive Particle Swam Optimization) are studied and compared. It is revealed that the APSO provides faster convergence and better search efficiency than the conventional PSO when they are adopted to find the global minimum of a two-dimensional function. The advantages of the APSO comes from the ability to control the inertia weight, and acceleration coefficients. To verify that the APSO is working better than the standard PSO, the design of a 10GHz microstrip patch as one of the elements of a high frequency array antenna is taken as a test-case and shows the optimized result with 5 iterations in the APSO and 28 iterations in th PSO.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.648-649
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• 2011

This Paper presents an efficient approach for solving the economic dispatch (ED) problems with valve-point effects using an combination of particle swarm optimization and harmony search. To reduce a premature convergence effect of PSO algorithm, We proposed PSO-HS algorithm considering evolutionary using harmony search algorithm. To prove the ability of the PSO-HS in solving nonlinear optimization problems, ED problems with non-convex solution spaces are solved with three different approach(PSO, HS, combination of PSO and HS)

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.291-298
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• 2020

This study proposed a model that can track MPP faster than the existing MPPT algorithm using the particle swarm optimization algorithm (PSO). The proposed model highly sets the acceleration constants of gbest and pbest in the PSO algorithm to quickly track the MPP point and eliminates the power instability problem. In addition, this algorithm was re-executed by detecting the change in power of the solar panel according to the rapid change in solar radiation. As a result of the experiment, MPP time was 0.03 seconds and power was 131.65 for 691.5 W/m2, and MPP was tracked at higher power and speed than the existing P&O and INC algorithms. The proposed model can be applied when a change in the amount of power is detected by partial shading in a Photovoltaic power plant with Photovoltaic connected in parallel. In order to improve the MPPT algorithm, this study needs a comparative study on optimization algorithms such as moth flame optimization (MFO) and whale optimization algorithm (WOA).

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.660-666
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• 2011

In this paper, we proposed a Particle Swarm Optimization(PSO) to search the optimal link lengths for legged walking robot. In order to apply the PSO algorithm for the proposed, its walking robot kinematic analysis is needed. A crab robot based on four-bar linkage mechanism and Jansen mechanism is implemented in H/W. For the performance index of PSO, the stride length of the legged walking robot is defined, based on the propose kinematic analysis. Comparative simulation results present to illustrate the viability and effectiveness of the proposed method.