We study two-stage robust optimization problems with mixed discrete-continuous decisions in both stages. Despite their broad range of applications, these problems pose two fundamental challenges: (i) they constitute infinite-dimensional problems that require a finite-dimensional approximation, and (ii) the presence of discrete recourse decisions typically prohibits dualitybased solution schemes...

This paper describes an object-oriented software system for continuous optimization by a new metaheuristic method, the Bat Algorithm, based on the echolocation behavior of bats. Bat algorithm was successfully used for many optimization problems and there is also a corresponding program in MATLAB. We implemented a modified version in C# which is easier for maintenance since it is object-oriented...

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The...

Abstract Most real optimization problems are defined over a mixed search space where the variables both discrete and continuous. In engineering applications, objective function is typically calculated with numerically costly black-box simulation. General therefore of great practical interest, yet their resolution remains in large part an open scientific question. this article, approached throug...

Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult real-world combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized A...

The combination of components from different algorithms is currently one of the most successful trends in optimization. The hybridization of metaheuristics such as ant colony optimization, evolutionary algorithms, and variable neighborhood search with techniques from operations research and artificial intelligence plays hereby an important role. The resulting hybrid algorithms are generally lab...

Abstract We study the problem of unsupervised learning graphical models in mixed discrete-continuous domains. The such discrete domains alone is notoriously challenging, compounded by fact that inference computationally demanding. situation generally believed to be significantly worse domains: estimating unknown probability distribution given samples often limited practice a handful parametric ...

This work presents a method for optimum design of structures, where the design variables can he considered as Continuous or discrete. The variables are chosen as sizing variables as well as coordinates of joints. The main idea is to reduce the number of structural analyses and the overal cost of optimization. In each design cycle, first the structural response quantities such as forces, displac...

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.