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In the present work, we propose a new variant of basic DE algorithm called CMDE-G which uses Cauchy mutation (CM) operator. In this algorithm, at the end of every generation, CM is applied as a local search mechanism to explore the neighborhood of the best individual in the population. The performance of CMDE-G algorithm is analyzed on a set of 10 standard benchmark problems and four nontraditional...
Invasive Weed Optimization Algorithm IWO) is an ecologically inspired metaheuristic that mimics the process of weeds colonization and distribution and is capable of solving multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. In this article a modified version of IWO has been used for training the feed-forward Artificial Neural Networks (ANNs) by adjusting the...
Differential Evolution (DE) is based on both an evolutionary strategy and a parallel direct search method employing a population. DE is an effective optimization method available for solving global optimization problem over continuous space. DE has a few control parameters that have to be set by users. This paper describes a new DE using Down-hill Simplex Method. Then we consider and examine average...
The Differential Evolution (DE) algorithm was initially proposed for continuous numerical optimization, but it has been applied with success in many combinatorial optimization problems, particularly permutation-based integer combinatorial problems. In this paper, a new and general approach for combinatorial optimization is proposed using the Differential Evolution algorithm. The proposed approach...
Many of the conventional Differential Evolutions (DEs) have employed the discrete generation model that uses two populations, namely, old one and new one. Recently, a new DE based on the continuous generation model is proposed. In the continuous generation model, only one population is used. The new DE is sometimes called Sequential DE (SDE). Besides better convergence, SDE has some advantages. For...
The parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model of the problem is often multimodal in nature and requires...
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