285 research outputs found
Building a Truly Distributed Constraint Solver with JADE
Real life problems such as scheduling meeting between people at different
locations can be modelled as distributed Constraint Satisfaction Problems
(CSPs). Suitable and satisfactory solutions can then be found using constraint
satisfaction algorithms which can be exhaustive (backtracking) or otherwise
(local search). However, most research in this area tested their algorithms by
simulation on a single PC with a single program entry point. The main
contribution of our work is the design and implementation of a truly
distributed constraint solver based on a local search algorithm using Java
Agent DEvelopment framework (JADE) to enable communication between agents on
different machines. Particularly, we discuss design and implementation issues
related to truly distributed constraint solver which might not be critical when
simulated on a single machine. Evaluation results indicate that our truly
distributed constraint solver works well within the observed limitations when
tested with various distributed CSPs. Our application can also incorporate any
constraint solving algorithm with little modifications.Comment: 7 page
Breakout Local Search for the Travelling Salesman Problem
The travelling salesman problem (TSP), a famous NP-hard combinatorial optimisation problem (COP), consists of finding a minimum length tour that visits n cities exactly once and comes back to the starting city. This paper presents a resolution of the TSP using the breakout local search metaheuristic algorithm (BLS), which is based on the iterated local search (ILS) framework and improves it by introducing some fundamental features of several well-established metaheuristics such as tabu search (TS) and variable neighbourhood search (VNS). BLS moves from a local optimum of a neighbourhood to another by applying perturbation jumps whose type and number are determined adaptively. It has already been applied to many COP and gives good results. This innovative hybridisation resolved well 41 instances from the commonly used benchmark library TSPLIB. The high quality of experimental results shows the competitiveness of the proposed algorithm compared to other algorithms based on local search
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
Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks
We present ULSA, a novel stochastic local search algorithm for random binary
constraint satisfaction problems (CSP). ULSA is many times faster than the
prior state of the art on a widely-studied suite of random CSP benchmarks.
Unlike the best previous methods for these benchmarks, ULSA is a simple
unweighted method that does not require dynamic adaptation of weights or
penalties. ULSA obtains new record best solutions satisfying 99 of 100
variables in the challenging frb100-40 benchmark instance
Hiding Satisfying Assignments: Two are Better than One
The evaluation of incomplete satisfiability solvers depends critically on the
availability of hard satisfiable instances. A plausible source of such
instances consists of random k-SAT formulas whose clauses are chosen uniformly
from among all clauses satisfying some randomly chosen truth assignment A.
Unfortunately, instances generated in this manner tend to be relatively easy
and can be solved efficiently by practical heuristics. Roughly speaking, as the
formula's density increases, for a number of different algorithms, A acts as a
stronger and stronger attractor. Motivated by recent results on the geometry of
the space of satisfying truth assignments of random k-SAT and NAE-k-SAT
formulas, we introduce a simple twist on this basic model, which appears to
dramatically increase its hardness. Namely, in addition to forbidding the
clauses violated by the hidden assignment A, we also forbid the clauses
violated by its complement, so that both A and complement of A are satisfying.
It appears that under this "symmetrization'' the effects of the two attractors
largely cancel out, making it much harder for algorithms to find any truth
assignment. We give theoretical and experimental evidence supporting this
assertion.Comment: Preliminary version appeared in AAAI 200
- …