• Journal of applied mathematics & informatics
• /
• v.20 no.1_2
• /
• pp.391-399
• /
• 2006

In order to solve the optimization problem of designing the trajectory of three-dimensional horizontal well, we establish a multi-phase, nonlinear, stochastic dynamic system of the trajectory of horizontal well. We take the precision of hitting target and the total length of the trajectory as the performance index. By the integration of the state equation, this model can be transformed into a nonlinear stochastic programming. We discuss here the necessary conditions under which a local solution exists and depends in a continuous way on the parameter (perturbation). According to the properties we propose a revised Hooke-Jeeves algorithm and work out corresponding software to calculate the local solution of the nonlinear stochastic programming and the expectancy of the performance index. The numerical results illustrate the validity of the proposed model and algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.12
• /
• pp.4799-4818
• /
• 2015

In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.

• Journal of Industrial Technology
• /
• v.15
• /
• pp.63-70
• /
• 1995

This paper presents an impact control algorithm for reducing the potentially damaging effects by interation of redundant manipulators with their environments. In the proposed control algorithm, the redundancy is resolved at the torque level by locally minimizing joint torque, subject to the operational space dynamic formulation which maps the joint torque set into the operational forces. For a given pre-impact velocity of the manipulator, the proposed approach is on generating joint space trajectories throughout the motion near the contact which instantaneously minimize the impulsive force which is a scalar function of manipulator's configurations. The comparative evaluation of the proposed algorithm with a local torque optimization algorithm with a local torque optimization algorithm without reducing impact is performed by computer simulation. The simulation results illustrate the effectiveness of the algorithm in reducing both the effects of impact and large torque requirements.

• Journal of applied mathematics & informatics
• /
• v.14 no.1_2
• /
• pp.123-136
• /
• 2004

In this paper, a combining trust region and line search algorithm for equality constrained optimization is proposed. At each iteration, we only need to solve the trust region subproblem once, when the trust region trial step can not be accepted, we switch to line search to obtain the next iteration. Hence, the difficulty of repeated solving trust region subproblem in an iterate is avoided. In order to allow the direction of negative curvature, we add second correction step in trust region step and employ nonmonotone technique in line search. The global convergence and local superlinearly rate are established under certain assumptions. Some numerical examples are given to illustrate the efficiency of the proposed algorithm.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.4 no.1
• /
• pp.110-122
• /
• 1996

The objective of this study is to understand the dynamic characterictics of OHV type valve trains and to design and optimal cam profile which will improve engine performance. A numerical model for valve train dynamics is presented, which aims at both accuracy and computational efficiency. The lumped mass model and distributed parameter model were used to describe the valve train dynamics. Nonlinear characterictics in the valve spring behavior were included in the model. Comprehensive experiments were carried out concerning the valve train dynamics, and the model was tuned based on the test results. The dynamic model was used in designing an optimal cam profile. Because the objective function has many local minima, a conventional local optimizer cannot be used to find an optimal solution. A modified adaptive random search method is successfully employed to solve the problem. Cam lobe area could be increased up to 7.3% without any penalties in kinematic and dynamic behaviors of the valve train.

• Journal of the Society of Naval Architects of Korea
• /
• v.42 no.3
• /
• pp.249-258
• /
• 2005

Recently, the vibration reduction at a local structure such as compass deck has been continuously requested by ship owner and shipbuilder. Because crews are afflicted with vibration, severe vibration problems even bring about a damage of structure. This study conducted to get an optimized stiffener size of compass deck to reduce the vibration level and decrease the weight of structure in ship. NASTRAN external call type optimization software (OptShip) which makes use of NASTRAN as a solver is used as an optimization tool. The results indicate that the optimum design is promising for real applications.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.5
• /
• pp.575-591
• /
• 2006

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.05a
• /
• pp.141-146
• /
• 1999

A new method, so called MMA(Method of Moving Asymptotes) was applied to the optimization problems of non-linear functions and non-linear structures. In each step of the iterative process, tile MMA generates a strictly convex approximation subproblems and solves them by using the dual problems. The generation of these subproblems is controlled by so called 'moving asymptotes', which may both make no oscillation and speed up tile convergence rate of optimization process. By contrast in generalized dual function, the generated function by MMA is always explicit type. Both the objective and behaviour constraints which were approximated are optimized by dual function. As the results of some examples, it was found that this method is very effective to obtain the global solution for problems with many local solutions. Also it was found that MMA is a very effective approximate method using the original function and its 1st derivatives.

• Journal of Information Processing Systems
• /
• v.18 no.3
• /
• 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 the Computational Structural Engineering Institute of Korea
• /
• v.35 no.6
• /
• pp.367-374
• /
• 2022

This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.