Layer Separation for Optimization of Composite Laminates

2008 ◽  
Author(s):  
Hossein Ghiasi ◽  
Damiano Pasini ◽  
Larry Lessard
Keyword(s):  
Composite Laminates ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Global Optimum ◽  
Important Variable ◽  
Optimization Techniques ◽  
Thin Layers ◽  
Stacking Sequence ◽  
Layer Separation ◽  
Number Of Layers

The excellent mechanical properties of laminated composites cannot be exploited without a careful design of stacking sequence of the layers. An important variable in the search of the optimum stacking sequence is the number of layers. The larger is this number, the harder as well as longer is the search for an optimal solution. To tackle efficiently such a variable-dimensional problem, we introduce here a multi-level optimization technique. The proposed method, called Layer Separation (LS), increases or decreases the number of layers by gradually separating a layer into two, or by merging two layers into one. LS uses different levels of laminate representation ranging from a coarse level parameterization, which corresponds to a small number of thick layers, to a fine level parameterization, which corresponds to a large number of thin layers. A benefit of such differentiation is an increase of the probability of finding the global optimum. In this paper, LS is applied to the design of composite laminates under single and multiple loadings. The results show that LS convergence rate is not inferior to that of other optimization techniques available in the literature. It is faster than an evolutionary algorithm, more efficient than a layerwise method, simple to perform, and it has the advantage of possibility of termination at any point during the optimization process.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

Optimal Setpoints for HVAC Systems via Iterative Cooperative Neighbor Communication

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Matthew Elliott ◽  
Bryan P. Rasmussen
Keyword(s):  
Energy Savings ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Hvac Systems ◽  
Global Optimum ◽  
Cost Information ◽  
Steady State Condition ◽  
Air Conditioning Systems ◽  
The Cost ◽  
Dynamic Subsystem

Heating, ventilation, and air conditioning systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized controller or widespread knowledge of the interaction dynamics between subsystems. Each subsystem's controller calculates an optimal steady state condition. The output corresponding to this condition is then communicated to downstream neighbors only. Similarly, each subsystem communicates to its upstream neighbors the predicted costs imposed by the neighbors' own calculated outputs. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum. Since each optimizer does not require knowledge of its neighbors' dynamics, changes in one controller do not require changes to all controllers in the network. Proofs of convergence to Pareto optimality under certain conditions are presented, and convergence under the approach is demonstrated with a simulation example. The approach is also applied to a laboratory-based water chiller system; several experiments demonstrate the features of the approach and potential for energy savings.

Swarm-Based Mean-Variance Mapping Optimization (MVMOS) for Solving Non-Convex Economic Dispatch Problems

2015 ◽  
pp. 211-251 ◽  
Author(s):  
Truong Hoang Khoa ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
Vo Ngoc Dieu
Keyword(s):  
Optimization Technique ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Mapping Function ◽  
Population Based ◽  
Optimization Techniques ◽  
Solution Quality ◽  
Complex Constraints ◽  
Global Optimal ◽  
Mean Variance

The practical Economic Dispatch (ED) problems have non-convex objective functions with complex constraints due to the effects of valve point loadings, multiple fuels, and prohibited zones. This leads to difficulty in finding the global optimal solution of the ED problems. This chapter proposes a new swarm-based Mean-Variance Mapping Optimization (MVMOS) for solving the non-convex ED. The proposed algorithm is a new population-based meta-heuristic optimization technique. Its special feature is a mapping function applied for the mutation. The proposed MVMOS is tested on several test systems and the comparisons of numerical obtained results between MVMOS and other optimization techniques are carried out. The comparisons show that the proposed method is more robust and provides better solution quality than most of the other methods. Therefore, the MVMOS is very favorable for solving non-convex ED problems.

Optimal Rescheduling of Generators for Congestion Management and Benefit Maximization in a Decentralized Bilateral Multi-transactions Power Network

2013 ◽  
Vol 14 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Brijesh Singh ◽  
Ranjit Mahanty ◽  
S.P. Singh
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Congestion Management ◽  
Global Optimum ◽  
Market Model ◽  
Optimal Dispatch ◽  
Optimum Solution ◽  
Decentralized Market

Abstract This paper presents a framework to achieve an optimal power flow solution in a decentralized bilateral multitransaction-based market. An independent optimal dispatch solution has been used for each market. The interior point (IP)-based optimization technique has been used for finding a global economic optimal solution of the whole system. In this method, all the participants try to maximize their own profits with the help of system information announced by the operator. In the present work, a parallel algorithm has been used to find out a global optimum solution in decentralized market model. The study has been carried out on a modified IEEE-30 bus system. The results show that the suggested decentralized approach can provide a better optimal solution. The obtained results show the effectiveness of IP optimization-based optimal generator schedule and congestion management in the decentralized market.

scholarly journals Mathematical Research on Optimization Technique for Diet Planning Problem: Case Research Autism Paralympic Athlete

2019 ◽  
Vol 8 (2S3) ◽  
pp. 387-391
Keyword(s):  
Operation Research ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Case Research ◽  
Diet Plan ◽  
Diet Problem ◽  
The Mathematical Model ◽  
The Ideal

This paper demonstrates distinctive methods used in operation research to experience with different diet issues. Every diet problem has its particular cost limitation and objective function. The designation of sufficient menus including the consideration of several types of constraints, for example, the ideal nutritional content, the amount of food to be consumed and others. The mathematical model is constructed to determine a diet plan as an optimal solution which fulfills every requirements and limitations. The application of different optimization techniques and weakness in each method has been reviewed. The use of integer programming and development that can be done also represents in this paper. An optimal and practical solution is acquired to solve the diet problem for autism Paralympic athlete

scholarly journals A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal
Keyword(s):  
Machine Learning ◽  
Design Space Exploration ◽  
Design Space ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Optimization Approach ◽  
Ic Engine ◽  
Weak Learner

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

Evaluation of global optimization methods for conceptual rainfall-runoff model calibration

1997 ◽  
Vol 36 (5) ◽  
pp. 53-60 ◽  
Author(s):  
V. A. Cooper ◽  
V. T. V. Nguyen ◽  
J. A. Nicell
Keyword(s):  
Global Optimization ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Global Optimum ◽  
Hydrologic Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Global Optimization Methods ◽  
Optimization Search

The calibration of conceptual rainfall runoff (CRR) models is an optimization problem whose objective is to determine the values of the model parameters which provide the best fit between observed and estimated flows. This study investigated the performance of three probabilistic optimization techniques for calibrating the Tank model, a hydrologic model typical of CRR models. These methods were the Shuffled Complex Evolution (SCE), genetic algorithms (GA) and simulated annealing (SA) methods. It was found that performances depended on the choice of the objective function considered and also an the position of the start of the optimization search relative to the global optimum. Of the three global optimization methods (GOM) in the study, the SCE method provided better estimates of the optimal solution than the GA and SA methods. Regarding the efficiency of the GOMs, as expressed by the number of iterations for convergence, the ranking in order of decreasing performance was the SCE, the GA and the SA methods.

scholarly journals Pseudodynamic Bearing Capacity Analysis of Shallow Strip Footing Using the Advanced Optimization Technique “Hybrid Symbiosis Organisms Search Algorithm” with Numerical Validation

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Arijit Saha ◽  
Apu Kumar Saha ◽  
Sima Ghosh
Keyword(s):  
Bearing Capacity ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Seismic Loading ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Strip Footing ◽  
Composite Failure ◽  
Engineering Problems ◽  
Dynamic Modules

The analysis of shallow foundations subjected to seismic loading has been an important area of research for civil engineers. This paper presents an upper-bound solution for bearing capacity of shallow strip footing considering composite failure mechanisms by the pseudodynamic approach. A recently developed hybrid symbiosis organisms search (HSOS) algorithm has been used to solve this problem. In the HSOS method, the exploration capability of SQI and the exploitation potential of SOS have been combined to increase the robustness of the algorithm. This combination can improve the searching capability of the algorithm for attaining the global optimum. Numerical analysis is also done using dynamic modules of PLAXIS-8.6v for the validation of this analytical solution. The results obtained from the present analysis using HSOS are thoroughly compared with the existing available literature and also with the other optimization techniques. The significance of the present methodology to analyze the bearing capacity is discussed, and the acceptability of HSOS technique is justified to solve such type of engineering problems.

Pareto Optimal Setpoints for HVAC Networks via Iterative Nearest Neighbor Communication

2013 ◽  
Author(s):  
Matthew S. Elliott ◽  
Christopher J. Bay ◽  
Bryan P. Rasmussen
Keyword(s):  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Hvac Systems ◽  
Global Optimum ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Cost Information ◽  
The Cost ◽  
Dynamic Subsystem

HVAC systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems in a well-defined local neighborhood. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized optimizer or widespread knowledge of the interaction dynamics between subsystems. Each subsystem’s optimizer communicates to its neighbors its calculated optimum setpoint, as well as the costs imposed by the neighbor’s calculated set-points. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum, and that changes in one controller do not require changes to all controllers in the network. Proofs of Pareto optimality are presented, and convergence under the approach is demonstrated with a numerical and experimental example.

Devolpment and Modeling of an Industrial Process Plan, Its Optimization using stochastic search Optimization Technique

2021 ◽  
Vol 15 ◽  
pp. 87-91
Author(s):  
Umer Asgher ◽  
Riaz Ahmad ◽  
Liaqat Ali
Keyword(s):  
Process Planning ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Optimization Technique ◽  
Industrial Process ◽  
Optimal Solution ◽  
Stochastic Search ◽  
Optimization Techniques ◽  
Process Plan ◽  
Search Optimization

Industrial process planning is principally an association between design and development or final production and has vital function in the manufacturing systems. In this paper the under research industry is security vehicle manufacturing industry in Pakistan. First of all a fundamental process plan is developed and then modeled mathematically using progressive closed loop approach. Mathematically modeled process plan is then optimized in order to find optimal or sub optimal solutions. Research then investigates the capability of an innovative optimization technique called stochastic search in handling optimization of manufacturing process plan. This new technique of stochastic, searches the best approximate process planning solution. Finally the research examines the convergence of optimization techniques to an optimal solution for a manufacturing framework.

Sign in / Sign up

Export Citation Format

Share Document