in this paper, a new robust controller based on geometric homogeneity and adaptive integral sliding mode is proposed for a class of second order systems. the upper bound of the system disturbances is not required. fully unknown parameters have been considered in the described model and its finite–time convergence to zero equilibrium point is proved. moreover, the controller is developed in the ...

in this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. the new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-pso and the shuffled sub-swarms particle optimization (sspso) technique. it is known that the θ-pso algorithm has better optimization performance than standard pso al...

This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust Particle Swarm algorithm coupled wi...

This study concerns a generic model-free stochastic optimization problem requiring the minimization of a risk function de%ned on a given bounded domain in a Euclidean space. Smoothness assumptions regarding the risk function are hypothesized, and members of the underlying space of probabilities are presumed subject to a large deviation principle; however, the risk function may well be nonconvex...

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs...

The optimization of multimodal functions is a challenging task, in particular when derivatives are not available for use. Recently, in a directional direct search framework, a clever multistart strategy was proposed for global derivative-free optimization of single objective functions. The goal of the current work is to generalize this approach to the computation of global Pareto fronts for mul...

This research is motivated by the need to design a biometric system that uses adaptive techniques relevant to a specific context to identify an individual with little or no interaction with the user. The aim of this research is to develop a framework for a context-aware adaptive multimodal biometric identification system using agent technology.

Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2006). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In th...

This paper describes the extension of the Alparon dialog manager to a broader domain and multiple modalities. To apply the dialog manager in the multimodal environment of the Personal Intelligent Travel Assistant (PITA), a multimodal coding scheme has been designed using dialog acts and XML. Additionally, the rigid database interface of the Alparon system has been replaced by a flexible and ada...

In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are found concurrently) techniques. Most resear...