Solving bi-objective flow shop problem with hybrid path relinking algorithm

This paper presents and investigates different ways to integrate path relinking techniques into the hypervolume-based multi-objective local search algorithm (HBMOLS). We aim to evaluate the effectiveness of different path relinking strategies, these strategies focus on two main steps: the ways of path generation and the mechanisms of solutions selection. We propose different methods to establish the path relinking algorithms in a multi-objective context. Computational results on a bi-objective flow shop problem (FSP) and a statistical comparison are reported in the paper. In comparison with two versions of HBMOLS, the algorithms selecting a set of solutions located in the middle of the generated path are efficient. The behavior of these algorithms sheds light on ways to further improvement

Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems

G. Luque, E. Alba, Parallel Hybrid Trajectory Based Metaheuristics for Real-World Problems, In Proceedings of Intelligent Networking and Collaborative Systems, pp. 184-191, 2-4 September, 2015, Taipei, Taiwan, IEEE PressThis paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We applied here this new model using a large benchmark of instances of two real-world NP-hard problems: DNA fragment assembly and QAP problems, with competitive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Path Relinking in Pareto Multi-objective Genetic Algorithms

Path relinking algorithms have proved their efficiency in single objective optimization. Here we propose to adapt this concept to Pareto optimization. We combine this original approach to a genetic algorithm. By applying this hybrid approach to a bi-objective permutation flow-shop problem, we show the interest of this approach. In this paper, we present first an Adaptive Genetic Algorithm dedicated to obtain a first well diversified approximation of the Pareto set. Then, we present an original hybridization with Path Relinking algorithm, in order to intensify the search between solutions obtained by the first approach. Results obtained are promising and show that cooperation between these optimization methods could be efficient for Pareto optimization

A Solution Merging Heuristic for the Steiner Problem in Graphs Using Tree Decompositions

Fixed parameter tractable algorithms for bounded treewidth are known to exist for a wide class of graph optimization problems. While most research in this area has been focused on exact algorithms, it is hard to find decompositions of treewidth sufficiently small to make these al- gorithms fast enough for practical use. Consequently, tree decomposition based algorithms have limited applicability to large scale optimization. However, by first reducing the input graph so that a small width tree decomposition can be found, we can harness the power of tree decomposi- tion based techniques in a heuristic algorithm, usable on graphs of much larger treewidth than would be tractable to solve exactly. We propose a solution merging heuristic to the Steiner Tree Problem that applies this idea. Standard local search heuristics provide a natural way to generate subgraphs with lower treewidth than the original instance, and subse- quently we extract an improved solution by solving the instance induced by this subgraph. As such the fixed parameter tractable algorithm be- comes an efficient tool for our solution merging heuristic. For a large class of sparse benchmark instances the algorithm is able to find small width tree decompositions on the union of generated solutions. Subsequently it can often improve on the generated solutions fast