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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)
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 studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the(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)
In this paper we investigate a self-adaptive differential evolution algorithm (<i>jDEdynNP-F</i>) where <i>F</i> and <i>CR</i> control parameters are self-adapted and a population size reduction method is used. Additionally the proposed <i>jDEdynNP-F</i> algorithm uses a mechanism for sign changing of <i>F</i> control parameter with some probability based(More)
In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original DE is(More)
In the paper an adapted version of the differential evolution algorithm has been created to solve a multi-objective optimization problem. Multi-objective Differential Evolution Algorithm using vector differences for perturbing the vector population with self adaptation is introduced. Through the combination of mutation strategies and self adaptation of(More)
Swarm-intelligence-based and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-and(More)