Browsing by Subject "Differential evolution"

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    Self-adaptive randomized and rank-based differential evolution for multimodal problems
    (Springer, 2011-01-15) Urfalioglu, O.; Arıkan, Orhan
    Differential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitness of the population. This paper presents a novel extension of DE called Randomized and Rank-based Differential Evolution (R2DE) and its self-adaptive version SAR2DE to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the population fitness. In extensive experiments conducted with a wide range of complexity settings, we show that the proposed heuristics lead to an overall improvement in robustness and speed of convergence compared to several global optimization techniques, including DE, Opposition based Differential Evolution (ODE), DE with Random Scale Factor (DERSF) and the self-adaptive Cauchy distribution based DE (NSDE).
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    Time-delay estimation in multiple-input single-output systems
    (IEEE, 2010) Koçak, Fatih; Gezici, Sinan
    In this paper, the time-delay estimation problem is studied for multiple-input single-output (MISO) systems. First, a theoretical analysis is carried out by deriving the Cramer-Rao lower bound (CRLB) for time-delay estimation in a MISO system. Then, the maximum likelihood (ML) estimator for the time-delay parameter is obtained, which results in a complex optimization problem in general. In order to provide a solution of the ML estimator with low computational complexity, ML estimation based on a genetic global optimization algorithm, namely, differential evolution (DE), is proposed. Simulation studies for various fading scenarios are performed to investigate the performance of the proposed algorithm. ©2010 IEEE.