• Advances in Computational Design
• /
• 제1권1호
• /
• pp.79-86
• /
• 2016

In this study, the three different meta-heuristics namely the Grey Wolf Optimizer (GWO), Stochastic Fractal Search (SFS), and Adaptive Differential Evolution with Optional External Archive (JADE) algorithms are examined. This study considers optimization of the planer frame to minimize its weight subjected to the strength and displacement constraints as per the American Institute of Steel and Construction - Load and Resistance Factor Design (AISC-LRFD). The GWO algorithm is associated with grey wolves' activities in the social hierarchy. The SFS algorithm works on the natural phenomenon of growth. JADE on the other hand is a powerful self-adaptive version of a differential evolution algorithm. A one-bay ten-story planar steel frame problem is examined in the present work to investigate the design ability of the proposed algorithms. The frame design is produced by optimizing the W-shaped cross sections of beam and column members as per AISC-LRFD standard steel sections. The results of the algorithms are compared. In addition, these results are also mapped with other state-of-art algorithms.

• Structural Monitoring and Maintenance
• /
• 제1권4호
• /
• pp.427-449
• /
• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• KIEE International Transactions on Electrophysics and Applications
• /
• 제4C권6호
• /
• pp.276-281
• /
• 2004

In this paper, the optimal design of a novel microstrip directional coupler with a grooved housing for high directivity characteristic is presented. It will be shown that the high directivity of the microstrip coupler can be achieved simply by attaching an optimized housing structure. over the microstrip, which is much easier to fabricate than other conventional types. The dimensions of the proposed structure are maximized by using (1+1) evolution strategy (ES) combined with the deterministic algorithm. To improve the effectiveness of the results, efficient optimization procedures suitable for the model are proposed. From these results, it is determined that the proposed structure indicates an improved directivity. The optimized results are verified by full wave analysis at the center frequency of 850MHz.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 D
• /
• pp.781-783
• /
• 1997

Genetic Algorithm is a kind of an evolution programming based on natural evolution principle. It applied to probabilistic searching, machine learning and optimization, and many good results were reported. Generator maintenance scheduling is an optimization Problem with constraints. This paper applied a genetic algorithm to generator maintenance scheduling problem and tested on sample systems. The results are compared with heuristic method and branch-and-bound method.

• Journal of Information Processing Systems
• /
• 제18권3호
• /
• pp.332-343
• /
• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• Journal of Electrical Engineering and Technology
• /
• 제3권4호
• /
• pp.476-483
• /
• 2008

This paper is aimed at presenting techniques of hybrid differential evolution for solving various kinds of Economic Dispatch(ED) problems such as those including prohibited zones, emission dispatch, multiple fuels, and multiple areas. The results obtained for typical problems are compared with those obtained by other techniques such as Particle Swarm Optimization(PSO) and Classical Evolutionary Programming(CEP) techniques. The comparison of the results proves that hybrid differential evolution is quite favorable for solving ED problems with no restrictions on the shapes of the input-output functions of the generator.

• KIEE International Transaction on Systems and Control
• /
• 제2D권2호
• /
• pp.125-134
• /
• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권4호
• /
• pp.402-409
• /
• 1999

Variable structure control law is well known to be a robust control algorithm and evolution strategy is used as an effective search algorithm in optimization problems. In this paper, we propose a variable structure controller with time-varying switching surface. We calculate the maximum value of seitching surface gradient that is of the 3rd order polynomial form. Evolution strategy is used to optimize the parameters of the switching surface gradient. Finally, the proposed method is applied to position tracking control for BLDC motor. Experimental results show that the proposed method is more useful than the conventional variable structure controller.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 하계학술대회 논문집 B
• /
• pp.932-934
• /
• 1993

Optimum pole shape is designed to increase the levitation and propulsion force of magnetic levitation systems. Evolution Strategy is introduced as optimization method. Evolution Strategy is random based non-deterministic method, developed by combining Genetic Algorithm with Simulated Annealing. Trasnsrapid-06, which was developed in Germany, is referenced model to be analyze. Design variables are nodes which determine fields pole shape of a linear synchronous motor, and the model analyzed by F.E.M.

• 유공압시스템학회논문집
• /
• 제2권3호
• /
• pp.1-5
• /
• 2005

There are two types of solenoid actuator for force and position control of the fluid power system. One is an on-off solenoid actuator and the other is an electro-magnetic proportional actuator. They have some different characteristics for attraction force according to solenoid shape. Attraction force of the on-off solenoid actuator only depends on flux density. And the stroke-force characteristics of the proportional solenoid actuator are determined by the shape of the control cone. In this paper, steady state characteristics of the solenoid actuator for electro-hydraulic proportional valve determined by the shape of control cone are analyzed using finite element method and it is confirmed that the proportional solenoid actuator has a constant attractive force in the control region independently on the stroke position. And the shape of control cone is optimized using 1+1 evolution strategy to get a constant force. In the optimization algorithm, control cone length, thickness and taper length are used as a design parameter.