Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which i...

Particle swarm optimization inspired with the social behavior in flocks of birds and schools of fish is an adaptive, stochastic and population-based optimization technique which was created by Kennedy and Eberhart in 1995 (9; 12). As one of the representatives of swarm intelligence (20), it has the distinctive characteristics: information exchange, intrinsic memory, and directional search in co...

Classifying and Counting Vehicles in Traffic Control Applications Francesco Archetti, Enza Messina, Daniele Toscani, Leonardo Vanneschi A Neural Evolutionary Classification Method for Brain-Wave Analysis Antonia Azzini, Andrea G.B. Tettamanzi Differential Evolution applied to a multimodal information theoretic optimization problem Patricia Besson, Jean-Marc Vesin, Vlad Popovici, Murat Kunt Arti...

Appropriately adapting mutation strategies is a challengeable problem of the literature of the Differential Evolution (DE). The Strategy adaptation Mechanism (SaM) can convert a control parameter adaptation algorithm to a strategy adaptation algorithm. To improve the quality of optimization result, the exploration property is important in the early stage of optimization process and the exploita...

Real world problems always have different multiple solutions. For instance, optical engineers need to tune the recording parameters to get as many optimal solutions as possible for multiple trials in the varied-line-spacing holographic grating design problem. Unfortunately, most traditional optimization techniques focus on solving for a single optimal solution. They need to be applied several t...

This paper proposes an improved particle swarm optimization (PSO). In order to increase the efficiency, suggestions on parameter settings is made and a mechanism is designed to prevent particles fall into the local optimal. To evaluate its effectiveness and efficiency, this approach is applied to multimodal function optimizing tasks. 16 benchmark functions were tested, and results were compared...

Multimodal optimization aims at finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable one while still maintaining the optimal system performance. Evolutionary Algorithms (EAs) due t...

this paper introduces a technique for controlling a class of uncertain chaotic systems using an adaptive fuzzy proportional-integrator-derivative (pid) controller with h∞ tracking performance. the purpose of this work is to achieve optimal tracking performance of the controller using backtracking search algorithm (bsa). bsa, which is a novel heuristic algorithm, has an easy structure with singl...

in this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. the estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. most researchers implemented nonlinear methods to extract the harmonic parameters. however, nonlinear methods for amplitude estimation increase time of convergence. hen...

Digital Talking Books (DTBs) offer to visually impaired users an evolution of analogue talking books that mimics the interaction possibilities of print books. They offer the possibility to randomly access book content, present images and tables, annotate books, amongst other features. However, current DTB players with their usability and accessibility problems, limit the enjoyment of the reader...