Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring search space possible graphs and moving towards graph that maximises given objective function. Some algorithms offer exact guarantee to return highest score, while others approximate in exchange for reduced computational complexity. This paper describes an BN learning algorithm, which we call Model A...

Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called “basin flooding”). We propose and evaluate a stochastic generalization of enforced hill-climbing for online use in goal-oriented probabilistic planning problems. We assume a provided heuristic function estimating expected cost to the goal with fla...

The up-link bandwidth in satellite networks and in advanced traffic wireless information system is very limited. A server broadcasts data files provided by different independent providers and accessed by many clients in a round-robin manner. The clients who access these files may have different patterns of access. Some clients may wish to access several files in any order (AND), some wish to ac...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of ...

Distributed hill-climbing algorithms are a powerful, practical technique for solving large Distributed Constraint Satisfaction Problems (DSCPs) such as distributed scheduling, resource allocation, and distributed optimization. Although incomplete, an ideal hill-climbing algorithm finds a solution that is very close to optimal while also minimizing the cost (i.e. the required bandwidth, processi...

The method of nearest-neighbor interchange effects local improvements in a binary tree by replacing a 4-subtree by one of its two alternatives if this improves the objective function. We extend this to k-subtrees to reduce the number of local optima. Possible sequences of k-subtrees to be examined are produced by moving a window over the tree, incorporating one edge at a time while deactivating...

This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is based on steepest ascent Hill Climbing. In contrast to many other approaches that use a metamodel for simulation optimisation, this algorithm alternates between the metamodel and its underlying simulation model, rather t...

The permutation flowshop scheduling problem with the objective of minimizing total flow time is known as a NP-hard problem, even for the two-machine cases. Because of the computational complexity of this problem, a multi-start simulated annealing (MSA) heuristic, which adopts a multi-start hill climbing strategy in the simulated annealing (SA) heuristic, is proposed to obtain close-to-optimal s...

Simulated annealing is a general optimisation algorithm, based on hill-climbing. As in hill-climbing, new candidate solutions are selected from the ‘neighbourhood’ of the current solution. For continuous parameter optimisation, it is practically impossible to choose direct neighbours, because of the vast number of points in the search space. In this case, it is necessary to choose new candidate...