in this paper presents a maximum power point tracking (mppt) technique based on the hill climbing search (hcs) method and fuzzy logic system for wind turbines (wts) including of permanent magnet synchronous generator (pmsg) as generator. in the conventional hcs method the step size is constant, therefor both steady-state response and dynamic response of method cannot provide at the same time an...

The coarse-to-fine search strategy is extensively used in current reported research. However, it has the same problems as any hill climbing algorithm, most importantly, it often finds local instead of global minima. Drawing upon the artificial intelligence literature, we applied an optimal graph search, namely A*, to the problem. Using real stereo and video test sets, we compared the A* method ...

Enforced hill climbing (EHC), a heuristicaa search method, has been frequently used in a number of AI planning systems. This paper presents a new form of EHC, guided enforced hill climbing (GEHC), to enhance EHC efficiency. Main feature in GEHC is an adaptive ordering function. GEHC has shown a significant improvement in EHC efficiency, especially when applied to larger problems.

The ability to track dynamic functional optima is important in many practical tasks. Recent research in this area has concentrated on modifying evolutionary algorithms (EAs) by triggering changes in control parameters, ensuring population diversity, or remembering past solutions. A set of results are presented that favourably compare hill climbing with a genetic algorithm, and reasons for the r...

Evolutionary algorithms and hill-climbing search models are investigated to address the protein structure prediction problem. This is a well-known NP-hard problem representing one of the most important and challenging problems in computational biology. The pull move operation is engaged as the main local search operator in several approaches to protein structure prediction. The considered appro...

In this paper, we investigate a family of hill-climbing procedures related to GSAT, a greedy random hill-climbing procedure for satissability. These procedures are able to solve large and diicult satissability problems beyond the range of conventional procedures like Davis-Putnam. We explore the r^ ole of greediness, randomness and hill-climbing in the eeectiveness of these procedures. We show ...

A number of non-exhaustive search algorithms are presented. The methods are a c̀lassical’ genetic algorithm, a tabu search, an evolutionary strategy and stochastically repeated nearest and steepest-ascent hill-climbing algorithms. They are then used to determine optimum and good polarities for Reed± Muller canonical expansions of Boolean functions, and comparisons are drawn between the relative ...

Consider the problem of control selection in complex dynamical and environmental scenarios where model predictive control (MPC) proves particularly effective. As the performance of MPC is highly dependent on the efficiency of its incorporated search algorithm, this work examined hill climbing as an alternative to traditional systematic or random search algorithms. The relative performance of a ...

Memetic algorithms (MAs) are meta-heuristics that join genetic algorithms with hill climbing. MAs have recognized success in solving difficult search and optimization problems. Hyperheuristics are proposed as an alternative to meta-heuristics. A hyperheuristic is a mechanism that chooses a heuristic from a set of heuristics, applies it to a candidate solution, and then makes a decision for acce...

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...