Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization ...
Abstract Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search al...
Amilkar Puris, Rafael Bello, Daniel Molina, Franci...
Abstract. Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identi...
We present two hybrid Metaheuristics, a hybrid Iterated Local Search and a hybrid Simulated Annealing, for solving real-world extensions of the Vehicle Routing Problem with Time Wi...
Tonci Caric, Juraj Fosin, Ante Galic, Hrvoje Gold,...
In this work, we explore the idea that parameter setting of stochastic metaheuristics should be considered as a multiobjective problem. The so-called “performance fronts” pres...