In global optimization, when the evaluation of the target function is costly, the usual strategy is to learn a surrogate model for the target function and replace the initial optimization by the optimization of the model. Gaussian processes have been widely used since they provide an elegant way to model the fitness and to deal with the explorationexploitation trade-off in a principled way. Sev...

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and recursive filter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to c...

This research paper presents a new evolutionary optimization model based on the particle swarm optimization (PSO) algorithm that incorporates the flocking behavior of a spider. The search space is divided into several segments like the net of a spider. The social information sharing among the swarms are made strong and adaptive. The main focus is on the fitness of the swarms adjusting to the le...

The hippocampus is a structure in the medial temporal lobe of the brain that is involved in episodic memory function. The texture features of the hippocampus could give better differentiation between Alzheimer’s disease and normal controls. The localization of the hippocampus structure in MRI histological images is considered as a multimodal global continuous optimization problem, which is solv...

This paper introduces a new technique called adaptive elitist-population search method. This technique allows unimodal function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals’ dissimilarity and a novel direction dependent elitist genetic operato...

This paper presents a new approach for the adaptive management of multimodal biometrics to meet a wide range of application dependent adaptive security requirements. In this work, ant colony optimization (ACO) is employed for the selection of key parameters like decision threshold and fusion rule, to ensure the optimal performance in meeting varying security requirements during the deployment o...

The problem of finding a global optimum of an unconstrained multimodal function has been the subject of intensive study in recent years, giving rise to valuable advances in solution methods. We examine this problem within the framework of adaptive memory programming (AMP), focusing particularly on AMP strategies that derive from an integration of Scatter Search and Tabu Search. Computational co...