Escaping local optima: constraint weights vs value penalties.

Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the constructive search techniques on large problems, but they have a propensity to converge to local optima. Therefore, a key research topic on iterative improvement search is the development of effective techniques for escaping local optima, most of which are based on increasing the weights attached to violated constraints. An alternative approach is to attach penalties to the individual variable values participating in a constraint violation. We compare both approaches and show that the penalty-based technique has a more dramatic effect on the cost landscape, leading to a higher ability to escape local optima. We present an improved version of an existing penalty-based algorithm where penalty resets are driven by the amount of distortion to the cost landscape caused by penalties. We compare this algorithm with an algorithm based on constraint weights and justify the difference in their performance

DisBO-wd: a distributed constraint satisfaction algorithm for coarse-grained distributed problems.

We present a distributed iterative improvement algorithm for solving coarse-grained distributed constraint satisfaction problems (DisCSPs). Our algorithm is inspired by the Distributed Breakout for coarse-grained DisCSPs where we introduce a constraint weight decay and a constraint weight learning mechanism in order to escape local optima. We also introduce some randomisation in order to give the search a better chance of finding the right path to a solution. We show that these mechanisms improve the performance of the algorithm considerably and make it competitive with respect to other algorithms

Distributed guided local search for solving binary DisCSPs.

We introduce the Distributed Guided Local Search (Dist- GLS) algorithm for solving Distributed Constraint Satisfaction Problems. Our algorithm is based on the centralised Guided Local Search algorithm, which is extended with additional heuristics in order to enhance its efficiency in distributed scenarios. We discuss the strategies we use for dealing with local optima in the search for solutions and compare performance of Dist-GLS with that of Distributed Breakout (DBA). In addition, we provide the results of our experiments with distributed versions of random binary constraint satisfaction and graph colouring problems

Escaping local optima with penalties in distributed iterative improvement search.

The advantages offered by iterative improvement search make it a popular technique for solving problems in centralised settings. However, the key challenge with this approach is finding effective strategies for dealing with local optima. Such strategies must push the algorithm away from the plateaux in the objective landscape and prevent it from returning to those areas. A wide variety of strategies have been proposed for centralised algorithms, while the two main strategies in distributed iterative improvement remain constraint weighting and stochastic escape. In this paper, we discuss the two phased strategy employed in Distributed Penalty Driven Search (DisPeL) an iterative improvement algorithm for solving Distributed Constraint Satisfaction problems. In the first phase of the strategy, agents try to force the search out of the local optima by perturbing their neighbourhoods; and use penalties, in the second phase, to guide the search away from plateaux if perturbation does not work. We discuss the heuristics that make up the strategy and provide empirical justification for their inclusion. We also present some empirical results using random non-binary problems to demonstrate the effectiveness of the strategy

Stoch-DisPeL: exploiting randomisation in DisPeL.

We present Stoch-DisPeL, an extension of the distributed constraint programming algorithm DisPeL which incorporates randomisation into the algorithm. We justify the introduction of stochastic moves and analyse its performance on random DisCSPs and on Distributed SAT problems. We also empirically compare Stoch-DisPeL's performance to that of DisPel and DSA-B1N - our improved version of DSA. The results obtained show a clear advantage of the introduction of random moves in DisPeL. Our new algorithm, Stoch-DisPeL, also performs better than DSA-B1N

Modifying landscapes with penalties in iterative improvement for solving distributed constraint satisfaction problems

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Solving DisCSPs with penalty-driven search.

We introduce the Distributed, Penalty-driven Local search algorithm (DisPeL) for solving Distributed Constraint Satisfaction Problems. DisPeL is a novel distributed iterative improvement algorithm which escapes local optima by the use of both temporary and incremental penalties and a tabu-like no-good store. We justify the use of these features and provide empirical results which demonstrate the competitiveness of the algorithm

Solving Coarse-grained DisCSPs with Multi-DisPeL and DisBO-wd

We present Multi-DisPel, a penalty-based local search distributed algorithm which is able to solve coarse-grained Distributed Constraint Satisfaction Problems (DisCSPs) efficiently. Multi-DisPeL uses penalties on values in order to escape local optima during problem solving rather than the popular weights on constraints. We also introduce DisBO-wd, a stochastic algorithm based on DisBO (Distributed Breakout) which includes a weight decay mechanism. We compare Multi-DisPeL and DisBO-wd with other algorithms and show, empirically, that they are more efficient and at least as effective as state of the art algorithms in some problem classes