Optimization techniques

The Effects of Different Kinds of Move in Differential Evolution Searches (2009)

by James Montgomery

4th Australian Conference on Artificial Life
http://dx.doi.org/10.1007/978-3-642-10427-5_27

Genetic algorithms applied to turbine extraction optimization of a pressurized-water reactor

by Wagner Sacco

The fuzzy clearing approach for a niching genetic algorithm applied to a nuclear reactor core design optimization problem

by Wagner Sacco

Two stochastic optimization algorithms applied to nuclear reactor core design

by Wagner Sacco

A parallel genetic algorithm with niching technique applied to a nuclear reactor core design optimization problem

by Wagner Sacco

A Metropolis Algorithm applied to a Nuclear Power Plant Auxiliary Feedwater System surveillance tests policy optimization

by Wagner Sacco

A niching genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization

by Wagner Sacco

A Metropolis algorithm combined with Nelder–Mead Simplex applied to nuclear reactor core design

by Wagner Sacco

Radiative properties estimation with the Particle Collision Algorithm based on a sensitivity analysis

by Wagner Sacco

Calculation of Critical Points of Thermodynamic Mixtures with Differential Evolution Algorithms

by Wagner Sacco

Differential evolution algorithms applied to nuclear reactor core design

by Wagner Sacco

Finding more than one root of nonlinear equations via a polarization technique: An application to double retrograde vaporization

by Wagner Sacco

Prediction of Double Retrograde Vaporization by Hybrid Global-Local Optimization Using Fuzzy Clustering Means

by Wagner Sacco

A Metropolis algorithm combined with Hooke-Jeeves local search method applied to global optimization

by Wagner Sacco

Finding all solutions of nonlinear systems using a hybrid metaheuristic with Fuzzy Clustering Means

by Wagner Sacco

Multidisciplinary design optimization of long endurance unmanned aerial vehicle wing

by Ranjan Ganguli

Rajagopal, S., and Ganguli, R., "Multidisciplinary design optimization of long endurance unmanned aerial vehicle wing", CMES - Computer Modeling in Engineering and Science

The preliminary wing design of a low speed, long endurance UAV is formulated as a two step optimization problem. The... more The preliminary wing design of a low speed, long endurance UAV is formulated as a two step optimization problem. The first step performs a single objective aerodynamic optimization and the second step involves a coupled dual objective aerodynamic and structural optimization. During the first step, airfoil geometry is optimized to get maximum endurance parameter at a 2D level with maximum thickness to chord ratio and maximum camber as design variables. Leading edge curvature, trailing edge radius, zero lift drag coefficient and zero lift moment coefficient are taken as constraints. Once the airfoil geometry is finalized, the wing planform parameters are optimized with minimization of wing weight and maximization of endurance. Four design variables from aerodynamics discipline namely taper ratio, aspect ratio, wing loading and wing twist are considered. Also, four more design variables from the structures discipline namely the upper and lower skin thicknesses at root and tip of the wing are added. Constraints are stall speed, maximum speed, rate of climb, strength and stiffness. The 2D airfoil and 3D wing aerodynamic analysis is performed by the XFLR5 panel method code and the structural analysis is performed by the MSC-NASTRAN finite element code. In the optimization process, a multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to discover the full Pareto front for the dual objective problem. In the second step, in order to reduce the time of computation, the analysis tools are replaced by a Kriging meta-model. For this dual objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the preliminary design of UAV wing.

Design and Optimization Method for Inertial Particle Separator Systems

by Eyas Alfaris

A High Speed and Performance Optimization Algorithm Based on Gravitational Approach

by Mina Sohrabi

Naji, H.R., Sohrabi, M., Rashedi, E., "A High Speed and Performance Optimization Algorithm Based on Gravitational Approach", Computing in Science and Engineering (ieee), 2011.

Recently a novel heuristic search algorithm, called Gravitational Search Algorithm (GSA), which is based on the law of... more Recently a novel heuristic search algorithm, called Gravitational Search Algorithm (GSA), which is based on the law of gravity and mass interactions, has been proposed. Although GSA has high performance in solving various optimization problems, it has some time consuming computations for calculation of the total force on each mass which makes the speed of optimization low.
In this paper we introduce a new approach, which improves GSA's speed considerably. Our approach is based on the multi-agent systems where multiple agents are the mechanism used to express the parallelism. In multi-agent based GSA, complex problems are decomposed into smaller and simpler components that are handled by different agents in the system. Our
experimental results show our multi-agent based GSA approach provides a high performance and high speed optimization methodology that can help scientists in a variety of science and engineering computations.

Multidisciplinary Design Optimization of Long Endurance

by Ranjan Ganguli

Rajagopal, S., and Ganguli, R., “Multidisciplinary Design Optimization of Long Endurance Unmanned Aerial Vehicle Wing”, Computer Modeling in Engineering and Science, Vol. 81, No. 1, 2011, pp. 1-34.

Ant Colony with Stochastic Local Search for the Quadratic Assignment Problem

by Malek Mouhoub

M. Mouhoub and Z. Wang. Ant Colony with Stochastic Local Search for the Quadratic Assignment Problem. The 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06). pages 143-149, Washington, November 13-15, 2006

The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined... more The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forward-looking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance, in terms of the quality of the solution returned, than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.