Journal ArticleDOI
Adaptive simulated annealing for optimization in signal processing applications
Sheng Chen,B. L. Luk +1 more
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TLDR
Three applications, maximum likelihood (ML) joint channel and data estimation, infinite-impulse-response (IIR) filter design and evaluation of minimum symbol-error-rate (MSER) decision feedback equalizer (DFE) are used to demonstrate the effectiveness of the ASA.About:
This article is published in Signal Processing.The article was published on 1999-11-30. It has received 142 citations till now. The article focuses on the topics: Adaptive simulated annealing & Simulated annealing.read more
Citations
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Journal ArticleDOI
A new design method based on artificial bee colony algorithm for digital IIR filters
TL;DR: A new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.
Journal ArticleDOI
A survey of simulated annealing as a tool for single and multiobjective optimization
Balram Suman,Prabhat Kumar +1 more
TL;DR: A comprehensive review of simulated annealing (SA)-based optimization algorithms, which solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima.
Book ChapterDOI
The Theory and Practice of Simulated Annealing
TL;DR: This chapter presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications, as well as recent advances in the analysis of finite time performance.
Journal ArticleDOI
Filter modeling using gravitational search algorithm
TL;DR: This paper is devoted to the presentation of a new linear and nonlinear filter modeling based on a gravitational search algorithm (GSA) where unknown filter parameters are considered as a vector to be optimized.
Journal ArticleDOI
Digital IIR filter design using differential evolution algorithm
TL;DR: Differential evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multimodal search space regardless of the initial parameter values, fast convergence, and using a few control parameters.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.