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...

Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowledge of dependencies in the data, the structure of a Bayesian network is learned from the data. Bayesian network structure learning is commonly posed as an optimization problem where search is used to find structures that maximize a scoring function. Since the structure search space is superexpon...

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 ...

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.

The freeform surface adaptive interferometer (FSAI) recently has been employed to realize the unknown metrology. A near null interferogram should be acquired from initial with undistinguished fringes even dark areas. direct optimization object in FSAI is just rather than focusing intensity characterization traditional wavefront-sensorless (WFS) systems. simulated annealing-hill climbing (SA-HC)...

Generalized hill climbing algorithms provide a framework for modeling several local search algorithms for hard discrete optimization problems. This paper introduces and analyzes generalized hill climbing algorithm performance measures that reflect how effectively an algorithm has performed to date in visiting a global optimum and how effectively an algorithm may perform in the future in visitin...

This paper is focused on introducing a hill-climbing algorithm as a way to solve the problem of generating typical testors -or non-reducible descriptorsfrom a training matrix. All the algorithms reported in the state-of-the-art have exponential complexity. However, there are problems for which there is no need to generate the whole set of typical testors, but it suffices to find only a subset o...

Rolling stock allocation is the process of assigning timetable schedules to physical train units. This is primarily done by connecting together schedules at their terminal locations (known as schedule associations). Platforming allocation is the process of assigning those associations to particular platforms. A simple last-in, first-legal-out algorithm is used for rolling stock allocation that ...

Reinforcement learning based on direct search in policy space requires few assumptions about the environment. Hence it is applicable in certain situations where most traditional reinforcement learning algorithms based on dynamic programming are not, especially in partially observable, deterministic worlds. In realistic settings, however, reliable policy evaluations are complicated by numerous s...