Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter ‘preference’ can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clus...

We consider the problem of stochastic monotone submodular function maximization, subject to constraints. We give results on adaptivity gaps, and on the gap between the optimal offline and online solutions. We present a procedure that transforms a decision tree (adaptive algorithm) into a non-adaptive chain. We prove that this chain achieves at least τ times the utility of the decision tree, ove...

The ABC algorithm has been used in many practical cases and has demonstrated good convergence rate. It produces the new solution according to the stochastic variance process. In this process, the magnitudes of the perturbation are important since it can affect the new solution. In this paper, we propose a self adaptive artificial bee colony, called self adaptive ABC, for the global numerical op...

in practice, obtaining the global optimum for the economic dispatch {bf (ed)}problem with ramp rate limits and prohibited operating zones is presents difficulties. this paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using afuzzy adaptive genetic algorithm (faga). the proposed algorithm  deals  with the issue ofcontrolling the ex...

The paper illustrates the application of a combined adaptive particle swarm optimization (A-PSO) algorithm and the finite strip method (FSM) to the lay-up optimization of symmetrically fiber-metal laminated (FML) composite shallow shell panels for maximizing the fundamental frequency. To improve the speed of the optimization process, adaptive inertia weight was used in the particle swarm optimiz...

Bayes-adaptive planning offers a principled solution to the explorationexploitation trade-off under model uncertainty. It finds the optimal policy in belief space, which explicitly accounts for the expected effect on future rewards of reductions in uncertainty. However, the Bayes-adaptive solution is typically intractable in domains with large or continuous state spaces. We present a tractable ...

Optimal control modification has been developed to improve robustness to model-reference adaptive control. For systems with linear matched uncertainty, optimal control modification adaptive law can be shown by a singular perturbation argument to possess an outer solution that exhibits a linear asymptotic property. Analytical expressions of phase and time delay margins for the outer solution can...

In an adaptive generalized sidelobe canceler interference signals are canceled when the reference inputs, or auxiliary beams, contain information about those interference signals while not containing information about the mainlobe signal of interest. Assuming a wide sense stationary scenario, the behavior and performance of this adaptive system approach the behavior and performance of the corre...

We present an adaptive wavelet method for the numerical solution of elliptic operator equations with nonlinear terms. This method is developed based on tree approximations for the solution of the equations and adaptive fast reconstruction of nonlinear functionals of wavelet expansions. We introduce a constructive greedy scheme for the construction of such tree approximations. Adaptive strategie...

We design adaptive high-order Galerkin methods for the solution of linear elliptic problems and study their performance. We start by considering adaptive Fourier-Galerkin methods and Legendre spectralelement methods, which offer unlimited approximation power only restricted by solution and data regularity. We next turn our attention to the $hp$-version of the finite element method, for which we...