Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief hi...

Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires that an optimization framework be defined whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecti...

Preface The interface between computer science and operations research has drawn much attention recently especially in optimization which is a main tool in operations research. In optimization area, the interest on this interface has rapidly increased in the last few years in order to develop nonstandard algorithms that can deal with optimization problems which the standard optimization techniq...

This paper deals with a general choice that one faces when developing an algorithm for a stochastic optimization problem: either design problem-specific algorithms that exploit the exact objective function, or to consider algorithms that only use estimated values of the objective function, which are very general and for which simple non-sophisticated versions can be quite easily designed. The P...

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...

This paper provides an overview of Software Project Scheduling problem as a combinatorial optimization problem. Since its inception by Alba, there have been multiple models to solve this problem. Metaheuristics provide high-level strategies capable of solving these problems efficiently. A set of metaheuristics used to solve this problem is presented, showing the resolution structure and its app...

The exploration of hybrid metaheuristics — combination of metaheuristics with concepts and processes from other research areas — has been an important trend in combinatorial optimization research. In this work, we developed a hybrid version of the GRASP metaheuristic which incorporates the path-relinking procedure — a memory-based intensification strategy — and a data mining module. Computation...

  The traveling salesman problem is a well-known and important combinatorial optimization problem. The goal of this problem is to find the shortest Hamiltonian path that visits each city in a given list exactly once and then returns to the starting city. In this paper, for the first time, the shortest Hamiltonian path is achieved for 1071 Iranian cities. For solving this large-scale problem, tw...

Quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems which can model many real life problems. Because of its theoretical and practical importance, QAP has attracted attention of many researchers. In this paper, a multi hybrid genetic algorithm for solving QAP is proposed. The key feature of our approach is the hybridization of three metaheuristics, tabu s...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...