Spatial orientation behavior is universal among animals, but its neuronal basis is poorly understood. The main objective of the present study was to identify candidate patterns of neuronal connectivity (motifs) for two widely recognized classes of spatial orientation behaviors: hill climbing, in which the organism seeks the highest point in a spatial gradient, and goal seeking, in which the org...

In this paper we consider the problem of three-dimensional scan registration for autonomous mobile vehicles. The problem of 3D scan matching is of enormous importance for the construction of metric representations of the environment, for localization, and for navigation planning in the three-dimensional space. We present a probabilistic technique that computes a probability density for each pai...

Constructive multi-start search algorithms are commonly used to address combinatorial optimization problems. Multi-start algorithms recover from local optima by restarting, which can lead to redundant configurations when search paths converge. In this paper, we investigate ways to minimize redundancy using record keeping and analyze several restart algorithms in the context of iterative hill cl...

Parameter tuning is an important step in automatic fuzzy model identification from sample data. It aims at the determination of quasi-optimal parameter values for fuzzy inference systems using an adequate search technique. In this paper, we introduce a new hybrid search algorithm that uses a variant of the cross-entropy (CE) method for global search purposes and a hill climbing type approach to...

We provide an improved algorithm called “a neighborhood expansion tabu search algorithm based on genetic factors” (NETS) to solve traveling salesman problem. The algorithm keeps the traditional tabu algorithm’s neighborhood, ensure the algorithm’s strong climbing ability and go to the local optimization. At the same time, introduce the genetic algorithm’s genetic factor (crossover factor and va...

The proliferation of wireless networks has underscored the need for systems capable of coping with sporadic network connectivity. The restriction of communication to neighboring hosts makes determining the global state especially difficult, if not impractical. This paper addresses the problem of coordinating the positions of an arbitrary number of services, encapsulated by mobile agents, in a d...

In the present paper we introduce new heuristic methods for the state minimization of nondeterministic finite automata. These methods are based on the classical Kameda-Weiner algorithm joined with local search heuristics, such as stochastic hill climbing and simulated annealing. The description of the proposed methods is given and the results of the numerical experiments are provided.

The Distributed Stochastic Algorithm (DSA) is a distributed hill-climbing technique for solving large Distributed Constraint Optimization Problems (DCOPs) such as distributed scheduling, resource allocation, and distributed route planning. The best known version of DSA, DSA-B, works by having agents change their assignments with probability p when making that change will improve their solution ...

In the study of graphical models, methods based on the concept of variational freeenergy bounds have been widely used for approximating functionals of probability distributions. In this paper, we provide a method based on the same principles that can be applied to problems of stochastic optimization. In particular, this method is based upon the same principles as the generalized EM algorithm. W...