Abstract In this paper a class of robust two-stage combinatorial optimization problems is discussed. It assumed that the uncertain second-stage costs are specified in form convex uncertainty set, particular polyhedral or ellipsoidal ones. shown versions basic network and selection NP-hard, even very restrictive cases. Some exact approximation algorithms for general problem constructed. Polynomi...

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz’s theory. Through the two-stage genetic optimization pr...

Particle swarm optimization (PSO) is a type of intelligence algorithm that frequently used to resolve specific global problems due its rapid convergence and ease operation. However, PSO still has certain deficiencies, such as poor trade-off between exploration exploitation premature convergence. Hence, this paper proposes dual-stage hybrid learning particle (DHLPSO). In the algorithm, iterative...

This paper presents an optimization design method for centrifugal compressors based on one-dimensional calculations and analyses. It consists of two parts: (1) centrifugal compressor geometry optimization based on one-dimensional calculations and (2) matching optimization of the vaned diffuser with an impeller based on the required throat area. A low pressure stage centrifugal compressor in a M...

In this work, a two-stage stochastic programming approach is implemented in a commercial simulator. A hybrid algorithm is proposed, where the first-stage decisions (existence of process units and their corresponding design parameters) are handled by a genetic algorithm, while the second-stage decisions (optimization of operational variables such as flows and temperatures) are optimized through ...

This paper presents an investigation on the computational complexity of stochastic optimization problems. We discuss a scenariobased model which captures the important classes of two-stage stochastic combinatorial optimization, two-stage stochastic linear programming, and two-stage stochastic integer linear programming. This model can also be used to handle chance constraints, which are used in...

This paper describes Stage, a learning algorithm that automatically improves search performance on large-scale optimization problems. Stage learns an evaluation function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited during search. The learned evaluation function is used to bias future search trajectories toward better opt...

Logic synthesis has two stages of optimization: technologyindependent and technology-dependent. This paper surveys state-of-the-art methods for estimation and optimization of delays of logic circuits at technology-independent stage. Although at this stage we cannot completely predict nal delays after technology mapping, there exist reasonably accurate estimation techniques. Final delays can be ...

In this paper, a brief summary of the heuristic methods, single-stage optimization methods, time-phased optimization methods, artificial intelligence (AI) techniques and iterative improvement methods are presented. Finally, some of the important characteristics of network programming methods and their strengths and weaknesses are identified and compared. Key-words Heuristic Methods, Single-Stag...

The optimization based automated analog design approach requires a global optimization technique that should satisfy both high success rate and high convergence rate for minimizing multiple design iterations. The traditional population-based optimization algorithms meet both the requirements. However, their performance depends on the values of the control parameters. Tuning the control paramete...