Manuel López-Ibáñez

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The program irace implements the iterated racing procedure, which is an extension of the Iterated F-race procedure (I/F-Race). Its main purpose is to automatically configure optimization algorithms by finding the most appropriate settings given a set of instances of an optimization problem. It builds upon the race package by Birattari and it is implemented(More)
The goal of multi-objective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multi-objective(More)
This chapter reviews the approaches that have been studied for the online adaptation of the parameters of ant colony optimization (ACO) algorithms, that is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this(More)
This paper presents a recursive, dimension-sweep algorithm for computing the hypervolume indicator of the quality of a set of n non-dominated points in d > 2 dimensions. It improves upon the existing HSO (Hypervolume by Slicing Objectives) algorithm by pruning the recursion tree to avoid repeated dominance checks and the recalculation of partial(More)
The travelling salesman problem with time windows is a difficult optimization problem that arises, for example, in logistics. This paper deals with the minimization of the travel-cost. For solving this problem, this paper proposes a Beam-ACO algorithm, which is a hybrid method combining ant colony optimization with beam search. In general, Beam-ACO(More)
We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the Robust Tabu Search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is(More)
In the last few years a significant number of ant colony optimization (ACO) algorithms have been proposed for tackling multi-objective optimization problems. In this paper, we propose a software framework that allows to instantiate the most prominent multiobjective ACO (MOACO) algorithms. More importantly, the flexibility of this MOACO framework allows the(More)
Over the last few years, there have been a number of proposals of ant colony optimization (ACO) algorithms for tackling multiobjective combinatorial optimization problems. These proposals adapt ACO concepts in various ways, for example, some use multiple pheromone matrices and multiple heuristic matrices and others use multiple ant colonies. In this(More)
Algorithms based on the two-phase local search (TPLS) framework are a powerful method to efficiently tackle multi-objective combinatorial optimization problems. TPLS algorithms solve a sequence of scalarizations, that is, weighted sum aggregations, of the multi-objective problem. Each successive scalarization uses a different weight from a predefined(More)
This paper presents new, carefully designed algorithms for five biobjective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) both, the weighted and nonweighted total tardiness of all jobs. The proposed algorithm combines two search(More)