A learning-based path relinking algorithm for the bandwidth coloring problem

This paper proposes a learning-based path relinking algorithm (LPR) for solving the bandwidth coloring problem and the bandwidth multicoloring problem. Based on the population path-relinking framework, the proposed algorithm integrates a learning-driven tabu optimization procedure and a path-relinking operator. LPR is assessed on two sets of 66 common benchmark instances, and achieves highly competitive results in terms of both solution quality and computational efficiency compared to the state-of-the-art algorithms in the literature. Specifically, the algorithm establishes 7 new upper bounds while matching the best known results for 56 cases. The impacts of the learning mechanism and the path relinking operators are investigated, confirming their critical role to the success of the proposed algorithm

Algorithms for the minimum sum coloring problem: a review

The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex coloring problem which has a number of AI related applications. Due to its theoretical and practical relevance, MSCP attracts increasing attention. The only existing review on the problem dates back to 2004 and mainly covers the history of MSCP and theoretical developments on specific graphs. In recent years, the field has witnessed significant progresses on approximation algorithms and practical solution algorithms. The purpose of this review is to provide a comprehensive inspection of the most recent and representative MSCP algorithms. To be informative, we identify the general framework followed by practical solution algorithms and the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the reviewed methods, we wish to encourage future development of more powerful methods and motivate new applications

Optimization Algorithms for Large-Scale Real-World Instances of the Frequency Assignment Problem

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the Frequency Assignment Problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real world instances of FAP typically involve very large networks, which can only be handled by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. In order to generate quickly and reliably high quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a population-based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.Publicad

Using GRASP and GA to design resilient and cost-effective IP/MPLS networks

The main objective of this thesis is to find good quality solutions for representative instances of the problem of designing a resilient and low cost IP/MPLS network, to be deployed over an existing optical transport network. This research is motivated by two complementary real-world application cases, which comprise the most important commercial and academic networks of Uruguay. To achieve this goal, we performed an exhaustive analysis of existing models and technologies. From all of them we took elements that were contrasted with the particular requirements of our counterparts. We highlight among these requirements, the need of getting solutions transparently implementable over a heterogeneous network environment, which limit us to use widely standardized features of related technologies. We decided to create new models more suitable to fit these needs. These models are intrinsically hard to solve (NP-Hard). Thus we developed metaheuristic based algorithms to find solutions to these real-world instances. Evolutionary Algorithms and Greedy Randomized Adaptive Search Procedures obtained the best results. As it usually happens, real-world planning problems are surrounded by uncertainty. Therefore, we have worked closely with our counterparts to reduce the fuzziness upon data to a set of representative cases. They were combined with different strategies of design to get to scenarios, which were translated into instances of these problems. Finally, the algorithms were fed with this information, and from their outcome we derived our results and conclusions

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

An Ant Colony Optimisation Algorithm for Timetabling Problem

The University Course Timetabling Problem (UCTP) is a combinatorial optimization problem which involves the placement of events into timeslots and assignment of venues to these events. Different institutions have their peculiar problems; therefore there is a need to get an adequate knowledge of the problem especially in the area of constraints before applying an efficient method that will get a feasible solution in a reasonable amount of time. Several methods have been applied to solve this problem; they include evolutionary algorithms, tabu search, local search and swarm optimization methods like the Ant Colony Optimisation (ACO) algorithm. A variant of ACO called the MAX-MIN Ant System (MMAS) is implemented with two local search procedures (one main and one auxiliary) to tackle the UCTP using Covenant University problem instance. The local search design proposed was tailored to suit the problem tackled and was compared with other designs to emphasise the effect of neighbourhood combination pattern on the algorithm performance. From the experimental procedures, it was observed that the local search design proposed significantly bettered the existing one used for the comparison. The results obtained by the implemented algorithm proved that metaheuristics are highly effective when tackling real-world cases of the UCTP and not just generated instances of the problem and can even be better if some tangible modifications are made to it to perfectly suit a problem domain