• 한국항해항만학회지
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• 제35권6호
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• pp.501-507
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• 2011

본 논문은 컨테이너 운송을 위한 AGV(Automated Guided Vehicle) 배차 전략을 대상으로 한다. AGV 배차 문제는 안벽 크레인의 대기 시간과 AGV의 주행 거리를 최소화하도록 AGV에 작업을 할당하는 것이 목표이다. 터미널 환경의 동적인 특성으로 인해 계획 결과의 정확한 예측이 어렵고 수정이 빈번하기 때문에 실무에서는 의사결정 시간이 짧은 단순 규칙 기반 배차가 많이 쓰인다. 그러나 단순 규칙 기반 배차는 근시안적 특성으로 인해 배차의 다양한 성능 지표를 만족시키지 못하는 한계가 있으며 이를 극복하기 위해 본 논문에서는 복수 규칙 기반의 배차 전략을 제안한다. 복수 휴리스틱 기반 배차 전략은 여러 규칙의 가중합으로 구성되며 규칙 사이의 가중치를 최적화하기 위해 다목적 진화 알고리즘을 적용하였다. 시뮬레이션 실험을 통해 제안 방안이 기존 단일 규칙 기반 배차에 비해 더 좋은 성능을 보임을 확인하였다.

• Smart Structures and Systems
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• 제33권5호
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• Transactions on Control, Automation and Systems Engineering
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• 제4권2호
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• pp.176-187
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• 2002

This paper focuses on the study of simulation and evolution of Micro Air Vehicles. Micro Air Vehicles or MAVs are small flying robots that are used for surveillance, search and rescue, and other missions. The simulated robots are designed based on realistic characteristics and the brains (controllers) of the robots are generated using genetic algorithms, i .e., simulated evolution. The objective for the experiments is to investigate the effects of robot team size and topology (simulation environment) on the evolution of simulated robots. The testing of team sizes deals with finding an ideal number of robots to be deployed for a given mission. The goal of the topology experiments is to see if there is an ideal topology (environment) to evolve the robots in order to increase their utility in most environments. We compare the results of the various experiments by evaluating the fitness values of the robots i .e., performance measure. In addition, evolved robot teams are tested in different situation in order to determine if the results can be generalized, and statistical analysis is performed to evaluate the evolved results.

• 한국군사과학기술학회지
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• 제19권1호
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• pp.95-104
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• 2016

To make a firing table of artillery with trajectory simulation, a precise trajectory model which corresponds with real firing test is required. Recent 4-DOF modified point mass trajectory model is considered accurate as a theoretical model, but fitting coefficients are used in calculation to match with real firing test results. In this paper, modified point mass trajectory model is presented and method of setting ballistic coefficient is introduced by applying optimization algorithms. After comparing two different algorithms, Particle Swarm Optimization and Covariance Matrix Adaptation - Evolutionary Strategy, we found that using CMA-ES algorithm gives fine optimization result. This fitting coefficient setting method can be used to make trajectory simulation which is required for development of new projectiles in the future.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제9권2호
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.69-72
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• 1996

This paper considers a global resolution of kinematic redundancy under inequality constraints as a constrained optimal control. In this formulation, joint limits and obstacles are regarded as state variable inequality constraints, and joint velocity limits as control variable inequality constraints. Necessary and sufficient conditions are derived by using Pontryagin's minimum principle and penalty function method. These conditions leads to a two-point boundary-value problem (TPBVP) with natural, periodic and inequality boundary conditions. In order to solve the TPBVP and to find a global minimum, a numerical algorithm, named two-stage algorithm, is presented. Given initial joint pose, the first stage finds the optimal joint trajectory and its corresponding minimum performance cost. The second stage searches for the optimal initial joint pose with globally minimum cost in the self-motion manifold. The effectiveness of the proposed algorithm is demonstrated through a simulation with a 3-dof planar redundant manipulator.

• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1155-1161
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• 2001

We propose an adaptive nonlinear control algorithm for high precision tracking and stabilization of LOS(Line-of-Sight). The friction parameters of the LOS gimbal are estimated by off-line evolutionary strategy and the friction is compensated by estimated friction compensator. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Lyapunov stability theory, and its convergence is guaranteed under the limited modeling error or torque disturbance. The performance of the pro-posed algorithm is verified by computer simulation on the LOS gimbal model of a moving vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

• Journal of Computing Science and Engineering
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• 제8권4호
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Journal of Advanced Marine Engineering and Technology
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• 제25권2호
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.