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...

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...

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...

This paper proposes a probabilistic hill-climbing algorithm, called PH, for the single-source transportation problem (STP). PH is tree search algorithm in which each node contains an assignment (AP) transformed from STP being solved. The transformation converts source’s product units into lots; lot equals multiple units. AP aims to find optimal of lots destinations minimize total cost. uses Hun...

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out ...

In many markets, customer preferences are context dependent. In the professional marketing literature, this dependence is typically recognized as a “need-state”.Moss and Edmonds (1997) recently reported a model that allows the testing of the qualitative judgements of domain experts in spirits markets against relevant EPOS data of product sales. This paper extends the use of context dependent cu...

This paper describes a successful adaptation of the Particle Swarm Optimization algorithm to discrete optimization problems. In the proposed algorithm, particles cycle through multiple phases with differing goals. We also exploit hill climbing. On benchmark problems, this algorithm outperforms a genetic algorithm and a previous discrete PSO formulation.