The rapid increase of dimensions and complexity of real life problems makes it more difficult to find optimal solutions by traditional optimization methods. This challenge requires intelligent and sophisticated algorithms to make the right decisions given a set of inputs and a variety of possible actions. In the problem solving arena, this definition is transformed into the term of artificial i...

Optimization algorithms inspired by the ants’ foraging behavior have been initially used for solving combinatorial optimization problems. Since the emergence of ant algorithms as an optimization tool, some attempts were also made to use them for tackling continuous optimization problems. In recent years, the investigation of synchronization and control problem for discrete chaotic systems has a...

In this thesis, we present a new theory of discrete constrained optimization using Lagrange multipliers and an associated first-order search procedure (DLM) to solve general constrained optimization problems in discrete, continuous and mixed-integer space. The constrained problems are general in the sense that they do not assume the differentiability or convexity of functions. Our proposed theo...

this paper presents a framework for long term transmission expansion planning in competitive, electricity markets. transmission lines and phase shifters are taken into account as expansion options. maximization of the network users' benefits, with satisfying security constraints are considered as the criterion for transmission expansion planning. the elements of the objective function are the...

For many years the mathematical programming community has realized the practical importance of developing algorithmic tools for tackling mixed integer optimization problems. Given our ability to solve linear optimization problems efficiently, one is inclined to think that a mixed integer program with both continuous and discrete variables is easier than a pure integer programming problem since ...

This article discusses solving non-linear programming problems containing integer, discrete and continuous variables. A novel mixed integer-discrete-continuous, non-linear optimization method based on Differential Evolution algorithm is described. Also the required handling techniques for integer, discrete and continuous variables are described including the techniques needed to handle boundary...

This paper treats the solution of nonlinear optimization problems involving discrete decision variables, also known as generalized disjunctive programming (GDP) or mixed-integer nonlinear programming (MINLP) problems, that arise in process engineering. The key idea is to eliminate the discrete decision variables by adding a set of continuous variables and constraints that represent the discrete...

optimization of reservoir parameters is an important issue in petroleum exploration and production. the ant colony optimization(aco) is a recent approach to solve discrete and continuous optimization problems. in this paper, the ant colony optimization is usedas an intelligent tool to estimate reservoir rock properties. the methodology is illustrated by using a case study on shear wave velocity...

Metaheuristics for discrete optimization can be thought of as dynamics on combinatorial landscapes, i.e., discrete energy landscapes. Since the features of a landscape determine the performance of a search algorithm, landscape analysis is a key tool in the selection and tuning of metaheuristics. While landscapes have been analyzed in this context, we believe there is room for further synthesis....