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—We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy(More)
The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original(More)
— This paper presents performance assessment of Differential Evolution for Multiobjective Optimization with Self Adaptation algorithm, which uses the self adaptation mechanism from evolution strategies to adapt F and CR parameters of the candidate creation in DE. Results for several runs on CEC2007 special session test functions are presented and assessed(More)
INTRODUCTION Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous Nowadays it is used as a(More)
In this paper we present experimental results to show deep view on how self-adaptive mechanism works in differential evolution algorithm. The results of the self-adaptive differential evolution algorithm were evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization. In this paper we(More)
—This paper presents a differential evolution (DE) algorithm for real-parameter optimization. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, combined with multiple mutation strategies using a structured population. The two mutation strategies used are run dependent on the population size,(More)
— In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in(More)