Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the
behavior of real ant colonies. In this paper, we review the underlying ideas of this approach …

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm
optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish …

The paper addresses the synergies from combining a heuristic method with a predictive
technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build …

Swarm intelligence is a relatively new approach to problem solving that takes inspiration
from the social behaviors of insects and of other animals. In particular, ants have inspired a …

Evolutionary algorithms often have to solve optimization problems in the presence of a wide
range of uncertainties. Generally, uncertainties in evolutionary computation can be divided …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between
two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

This paper provides a survey of the research done on optimization in dynamic environments
over the past decade. We show an analysis of the most commonly used problems, methods …

This chapter presents an overview of ant colony optimization (ACO)—a metaheuristic
inspired by the behavior of real ants. ACO was proposed by Dorigo et al. as a method for …