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A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems

Garrido P., Castro C.

Fundamenta Informaticae

2012

Vol. 119, nr 1

29-60

We present a self-adaptive hyper-heuristic capable of solving static and dynamic instances of the capacitated vehicle routing problem. The hyper-heuristic manages a generic sequence of constructive and perturbative low-level heuristics, which are gradually applied to construct or improve partial routes. We present some design considerations to allow the collaboration among heuristics, and to find the most promising sequence. The search process is carried out by applying a set of operators which constructs new sequences of heuristics, i.e., solving strategies. We have used a general and low-computational cost parameter control strategy, based on simple reinforcement learning ideas, to assign non-arbitrary reward/penalty values and guide the selection of operators. Our approach has been tested using some standard state-of-the-art benchmarks, which present different topologies and dynamic properties, and we have compared it with previous hyper-heuristics and several well-known methods proposed in the literature. The experimental results have shown that our approach is able to attain quite stable and good quality solutions after solving various problems, and to adapt to dynamic scenarios more naturally than other methods. Particularly, in the dynamic case we have obtained high-quality solutions when compared with other algorithms in the literature. Thus, we conclude that our self-adaptive hyper-heuristic is an interesting approach for solving vehicle routing problems as it has been able (1) to guide the search for appropriate operators, and (2) to adapt itself to particular states of the problem by choosing a suitable combination of heuristics.

Building Constraint Satisfaction Problem solvers using

Castro C.

Fundamenta Informaticae

1998

Vol. 34, nr 3

263-293

In this paper, we formalize Constraint Satisfaction Problem manipulation using a rule-based approach. Based on the notion of Computational Systems, we associate basic transformations carried out by traditional constraint solving algorithms with rewrite rules, and heuristics with strategies establishing the order of application of the inferences. In this way, a constraint solver can be viewed as a computational system aimed to transform a set of constraints in a particular solved from. The distinction made between deduction rules and strategies, allows to describe constraint handling in a very abstract way, prototype new heuristics almost by modifying only the choice of rules, prove termination in an easier way, and combine constraint solving with other computational systems. To validate our approach we have implemented the system COLETTE which is currently executable in ELAN, an environment for prototyping computational systems. We have realized how easy it is to integrate and reuse solvers developed following this approach. We hope that this work leads the way to integrating the knowledge existing in the domain of Automated Deduction and Constraint Solving