Pressurized water reactor in-core nuclear fuel management by tabu search

Optimization of the arrangement of fuel assemblies and burnable poisons when reloading pressurized water reactors has, in the past, been performed with many di erent algorithms in an attempt to make reactors more economic and fuel effi cient. The use of the tabu search algorithm in tackling reload core design problems is investigated further here after limited, but promising, previous investigations. The performance of the tabu search implementation developed was compared with established genetic algorithm and simulated annealing optimization routines. Tabu search outperformed these existing programs for a number of dffi erent objective functions on two di erent representative core geometries.This is the accepted manuscript. It is published by Elsevier here: http://www.sciencedirect.com/science/article/pii/S030645491400382X

Molecular Design of Crosslinked Copolymers

A complete methodology for the computational molecular design (CMD) of crosslinked polymers is developed and implemented. The methodology is applied to the design of novel polymers for restorative dental materials. The computational molecular design of crosslinked polymers using optimization techniques is a new area of research. The first part of this project seeks to develop a novel data structure capable of adequately storing a complete description of the crosslinked polymer structure. Numerical descriptors of polymer structure are then calculated from the data structure. Statistical methods are used to relate the structural descriptors to experimentally measured properties. An important part of this project is to show that useful property prediction models can be developed for crosslinked polymers. Desirable property target values are then set for a specific application. Finally, the structure-property relations are combined with a Tabu search optimization algorithm to design improved polymers. Tabu search allows much flexibility in the problem formulations, so a major goal of this project is to show that Tabu search is a effective method for crosslinked polymer design. To implement the molecular design procedure, a software package is developed. The software allows for easy graphical entry of polymer structures and property data, and contains a Tabu search optimization routine. Since computational molecular design of crosslinked polymers is a relatively new area of research, the software is designed to be easily modified to allow for extensive numerical experimentation. Finally, the computational design methodology is demonstrated for the design of polymers for restorative dental applications. Using the computational molecular design methodology developed in this project, several monomers are found that may offer a significant improvement over a standard HEMA/bisGMA formulation. The results of the case study show that the new data structure for crosslinked polymers is effective for calculation of topological descriptors and roperty models can be developed for crosslinked polymers. Tabu search is also shown to be an effective optimization method

3D Protein structure prediction with genetic tabu search algorithm

Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively

TOWARDS A UNIFIED VIEW OF METAHEURISTICS

This talk provides a complete background on metaheuristics and presents in a unified view the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. The key search components of metaheuristics are considered as a toolbox for: - Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems. - Designing efficient metaheuristics for multi-objective optimization problems. - Designing hybrid, parallel and distributed metaheuristics. - Implementing metaheuristics on sequential and parallel machines

TOWARDS A UNIFIED VIEW OF METAHEURISTICS

This talk provides a complete background on metaheuristics and presents in a unified view the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. The key search components of metaheuristics are considered as a toolbox for: - Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems. - Designing efficient metaheuristics for multi-objective optimization problems. - Designing hybrid, parallel and distributed metaheuristics. - Implementing metaheuristics on sequential and parallel machines

New heuristic-based design of robust power system stabilizers

This paper proposes a new robust design of power system stabilizers (PSSs) in a multimachine power system using a heuristic optimization method. The structure of each PSS used is similar to that of a conventional lead/lag stabilizer. The proposed design regards a multimachine power system with PSSs as a multi-input multi-output (MIMO) control system. Additionally, a multiplicative uncertainty model is taken into account in the power system representation. Accordingly, the robust stability margin can be guaranteed by a multiplicative stability margin (MSM). The presented method utilizes the MSM as the design specification for robust stability. To acquire the control parameters of PSSs, a control design in MIMO system is formulated as an optimization problem. In the selection of objective function, not only disturbance attenuation performance but also robust stability indices are considered. Subsequently, the hybrid tabu search and evolutionary programming (hybrid TS/EP) is employed to search for the optimal parameters. The significant effects of designed PSSs are investigated under several system operating conditions

A Generalization Model and Learning in Hardware

We study two problems in the field of machine learning. First, we propose a novel theoretical framework for understanding learning and generalization which we call the bin model. Using the bin model, a closed form is derived for the generalization error that estimates the out-of-sample performance in terms of the in-sample performance. We address the problem of overfitting, and show that using a simple exhaustive learning algorithm it does not arise. This is independent of the target function, input distribution and learning model, and remains true even with noisy data sets. We apply our analysis to both classification and regression problems and give an example of how it may be used efficiently in practice. Second, we investigate the use of learning and evolution in hardware for digital circuit design. Using the reactive tabu search for discrete optimization, we show that we can learn a multiplier circuit from a set of examples. The learned circuit makes less than 2% error and uses fewer chip resources than the standard digital design. We compare use of a genetic algorithm and the reactive tabu search for fitness optimization and show that the reactive tabu search performs significantly better on a 2-bit adder design problem for a similar execution time

Intensification-driven tabu search for the minimum differential dispersion problem

The minimum differential dispersion problem is a NP-hard combinatorial optimization problem with numerous relevant applications. In this paper, we propose an intensification-driven tabu search algorithm for solving this computationally challenging problem by integrating a constrained neighborhood, a solution-based tabu strategy, and an intensified search mechanism to create a search that effectively exploits the elements of intensification and diversification. We demonstrate the competitiveness of the proposed algorithm by presenting improved new best solutions for 127 out of 250 benchmark instances (>50%). We study the search trajectory of the algorithm to shed light on its behavior and investigate the spatial distribution of high-quality solutions in the search space to motivate the design choice of the intensified search mechanism

Learning Models for Discrete Optimization

We consider a class of optimization approaches that incorporate machine learning models into the algorithm structure. Our focus is on the algorithms that can learn the patterns in the search space in order to boost computational performance. The idea is to design optimization techniques that allow for computationally efficient tuning a priori. The final objective of this work is to provide efficient solvers that can be tuned for optimal performance in serial and parallel environments.This dissertation provides a novel machine learning model based on logistic regression and describes an implementation for scheduling problems. We incorporate the proposed learning model into a well-known optimization algorithm, tabu search, and demonstrate the potential of the underlying ideas. The dissertation also establishes a new framework for comparing optimization algorithms. This framework provides a comparison of algorithms that is statistically meaningful and intuitive. Using this framework, we demonstrate that the inclusion of the logistic regression model into the tabu search method provides significant boost of its performance. Finally, we study the parallel implementation of the algorithm and evaluate the algorithm performance when more connections between threads exist