Flexible molecular superposition: development of a combined similarity index and application of the constrained optimization technique

2001 ◽  
Vol 22 (8) ◽  
pp. 888-900 ◽  
Author(s):  
Chong Hak Chae ◽  
Dong Gweon Oh ◽  
Whanchul Shin
Keyword(s):  
Constrained Optimization ◽  
Optimization Technique ◽  
Similarity Index ◽  
Molecular Superposition

Swift — a new constrained optimization technique

1975 ◽  
Vol 6 (3) ◽  
pp. 309-317 ◽  
Author(s):  
B.V. Sheela ◽  
P. Ramamoorthy
Keyword(s):  
Constrained Optimization ◽  
Optimization Technique

scholarly journals Correction of failure in linear antenna arrays with greedy sparseness constrained optimization technique

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189240 ◽  
Author(s):  
Shafqat Ullah Khan ◽  
M. K. A. Rahim ◽  
Murtala Aminu-Baba ◽  
N. A. Murad
Keyword(s):  
Constrained Optimization ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Linear Antenna ◽  
Linear Antenna Arrays

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2012 ◽  
pp. 643-662
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

2011 ◽  
pp. 212-231 ◽  
Author(s):  
Alexandros Xanthopoulos ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Objective Function ◽  
Penalty Function ◽  
Manufacturing Systems ◽  
Optimization Technique ◽  
Hybrid Genetic Algorithm ◽  
Decision Variable ◽  
Exponential Penalty Function ◽  
Simulation Scenario

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

WHOLE-BODY MOTION GENERATION OF ANDROID ROBOT USING MOTION CAPTURE AND NONLINEAR CONSTRAINED OPTIMIZATION

2013 ◽  
Vol 10 (02) ◽  
pp. 1350003 ◽  
Author(s):  
JUNG-YUP KIM ◽  
YOUNG-SEOG KIM
Keyword(s):  
Constrained Optimization ◽  
Motion Capture ◽  
Optimization Technique ◽  
Human Motion ◽  
Body Motion ◽  
Whole Body ◽  
Motion Generation ◽  
Nonlinear Constrained Optimization ◽  
Gimbal Lock ◽  
Android Robot

This paper describes a whole-body motion generation scheme for an android robot using motion capture and an optimization method. Android robots basically require human-like motions due to their human-like appearances. However, they have various limitations on joint angle, and joint velocity as well as different numbers of joints and dimensions compared to humans. Because of these limitations and differences, one appropriate approach is to use an optimization technique for the motion capture data. Another important issue in whole-body motion generation is the gimbal lock problem, where a degree of freedom at the three-DOF shoulder disappears. Since the gimbal lock causes two DOFs at the shoulder joint diverge, a simple and effective strategy is required to avoid the divergence. Therefore, we propose a novel algorithm using nonlinear constrained optimization with special cost functions to cope with the aforementioned problems. To verify our algorithm, we chose a fast boxing motion that has a large range of motion and frequent gimbal lock situations as well as dynamic stepping motions. We then successfully obtained a suitable boxing motion very similar to captured human motion and also derived a zero moment point (ZMP) trajectory that is realizable for a given android robot model. Finally, quantitative and qualitative evaluations in terms of kinematics and dynamics are carried out for the derived android boxing motion.

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