Advances in Metaheuristics for Hard Optimization

A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation

Parallel Local Search

Local search metaheuristics are a recognized means of solving hard combinatorial problems. Over the last couple of decades, significant advances have been made in terms of the formalization, applicability and performance of these methods. Key to the performance aspect is the increased availability of parallel hardware, which turns out to be largely exploitable by this class of procedures. As re...

Hidden Markov Models Training Using Population-based Metaheuristics

In this chapter, we consider the issue of Hidden Markov Model (HMM) training. First, HMMs are introduced and then we focus on the particular HMMs training problem. We emphasize the difficulty of this problem and present various criteria that can be considered. Many different adaptations of metaheuristics have already been used but, until now, a few extensive comparisons have been performed on t...

Learning Structure Illuminates Black Boxes - An Introduction to Estimation of Distribution Algorithms

This chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit certain structural properties of optimization problems. State-of-the-art EDAs consistently outperform classical g...

Population-Based Metaheuristics: A Comparative Analysis

To optimally solve hard optimization problems in real life, many methods were designed and tested. The metaheuristics proved to be the generally adequate techniques, while the exact traditional optimization mathematical methods are prohibitively expensive in computational time. The population-based metaheuristics, which manipulate a set of candidate solutions at a time, have advantages over the...