this study concerns with a trust-region-based method for solving unconstrained optimization problems. the approach takes the advantages of the compact limited memory bfgs updating formula together with an appropriate adaptive radius strategy. in our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-newt...

The objective of this thesis is to investigate how to improve Particle Swarm Optimization by hybridization of stochastic search heuristics and by a Self-Organized Criticality extension. The thesis will describe two hybrid models extending Particle Swarm Optimization with two aspects from Evolutionary Algorithms, recombination via breeding and gene flow restriction via subpopulations. A further ...

Seed Throwing Optimization is an easy to implement probabilistic metaheuristic for multimodal function optimization with roots in hill climbing and the evolutionary computation like technique Harmony Search. It is a randomized Gradient Ascent with multiple initial states and the possibility to limit exploration to only paths which have shown potential. In this paper, the speed of convergence of...

The problem of finding a global optimum of a constrained multimodal function has been the subject of intensive study in recent years. Several effective global optimization algorithms for constrained problems have been developed; among them, the multistart procedures discussed in Ugray et al. (2007) are the most effective. We present some new multistart methods based on the framework of adaptive...

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

This paper provides an overview of the aims of the TALK project focusing on the issue of integrating Reinforcement Learning (RL) with the Information State Update (ISU) approach to dialogue management, in order to develop adaptive multimodal dialogue systems. The project will build showcases for in-car and in-home information and control, but its main aim is to advance our understanding of gene...

Adaptive infinite impulse response filters require the exploration of a multimodal error surface. We describe the use of genetic algorithms for this particular problem and demonstrate that our approach is capable of discovering the global minimum of the performance surface of a multimodal adaptive filter example in a reasonable length of time compared with other methods. We also compare the per...

In this paper, intelligent expert agent-based architectures for multimedia multimodal dialog protocols are proposed. The full architecture is implemented to help disabled people to access Web services and common computer equipment. The generic components of the application are then monitored by an expert agent, which can then perform dynamic changes in reconfiguration, adaptation and evolution ...

As a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization. However, traditional Gaussian EDA (GEDA) may suffer from premature convergence and has a high risk of falling into local optimum when dealing with multimodal problem. In this paper, we first attempts to improve the performance o...

A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulati...