A metaheuristic is an intelligent, iterative process that guides a search and can be applied towards optimization problems, such as the Traveling Salesman Problem. Two well studied techniques for solving optimization problems are Genetic Algorithms and Ant Colony Systems. However, each metaheuristic has different strengths and weaknesses. Genetic Algorithms are able to quickly find near optimal...

The problem of detecting suitable parameters for metaheuristic optimization algorithms is well known long since. As these nondeterministic methods, e.g. evolution strategies (ES) [1], are highly adaptible to a specific application, detecting good parameter settings is vital for their success. Performance differences of orders of magnitude (in time and/or quality) are often achieved by means of ...

Metaheuristics are computational procedures that intelligently lead the search process through efficient exploration of space associated with an optimization problem. With progressive outburst problems large data sets in various fields, there is ongoing quest for enhancing existing metaheuristic algorithms as well developing new ones greater accuracy and efficiency. In general, a powerful algor...

Metaheuristic algorithms are traditionally designed following a manual, iterative algorithm development process. While this process sometimes leads to high performing algorithms, it is labor-intensive, error-prone, difficult to reproduce, and explores only a limited number of design alternatives. In this article, we advocate the automatic design of hybrid metaheuristic algorithms. For an effect...

Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization paradigm) which combines the previously developed algorithms S-ACO and P-ACO, and SPSA, which extends Pareto Simulated Annealing to the stochastic case. Both approaches are tested on random instances of a TSP with tim...

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance...