Agent based models (ABM) have been recently applied to solve optimization problems
whose domains present several inter-related components in a distributed and …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

Recently, storage systems have observed a great leap in performance, reliability,
endurance, and cost, due to the advance in non-volatile memory technologies, such as …

Memetic computation is a paradigm that uses the notion of meme (s) as units of information
encoded in computational representations for the purpose of problem-solving. It covers a …

Traditional solvers for tackling combinatorial optimization (CO) problems are usually
designed by human experts. Recently, there has been a surge of interest in utilizing deep …

In this paper, we present a new method, called the genetic simulated annealing ant colony
system with particle swarm optimization techniques, for solving the traveling salesman …

This work presents a new metaheuristic for the euclidean Traveling Salesman Problem
(TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five …

Abstract The design of Ant Colony Optimization (ACO) has been inspired by the foraging
behavior of ant colonies. ACO is one of the most widely used metaheuristic algorithms …

Abstract The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have been …

In this paper, we propose a general agent-based distributed framework where each agent is
implementing a different metaheuristic/local search combination. Moreover, an agent …