Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm (EA) for the global optimization domain. But DE is not completely free from the problems of slow and/or premature convergence. In this paper, an improving clustering-based differential evolution with chaotic sequences and new mutation operator (CCDE) is proposed for the unconstrained global optimization p...

For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computatio...

In this paper, a newly-proposed algorithm based on 3-Parents Differential Evolution (3PDE) is implemented to be self-adapted for the only control parameter of scaling factor, F. It is called 3Parents DE with Self-adaptive Scaling Factor (SaFDE). The performance of this proposed algorithm is compared against to the original Differential Evolution (DE). In this paper, 50 runs are conducted for ev...

From a learning perspective, the mutation scheme in differential evolution (DE) can be regarded as a learning strategy. When mutating, three random individuals are selected and placed in a random order. This strategy, however, probably suffers some drawbacks which can slow down the convergence rate. To improve the efficiency of classic DE, this paper proposes a differential evolution based on i...

The Nelder-Mead Algorithm (NMA) is an almost half-century old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are recently introduced formal generalization of traditional PSO and DE that apply naturally to both continuous and...

Crossover rate (CR) is a key parameter affecting the operation of differential evolution (DE). According to the different status appear in CR adaptive process, the present paper employs power mean averaging operators to improve the value of CR in appropriate chance and propose a Power Mean based Crossover Rate Adaptive Differential Evolution (PMCRADE). The performance of PMCRADE is evaluated on...

The reactive power planning and dispatch problems have been solved using Genetic algorithm (GA), Differential evolution (DE) and Particle Swarm Optimization (PSO) technique in order to have a comparative study on the performance of these algorithms. It has been found that Differential evolution performs best followed by the Particle swarm optimization. Both DE and PSO can perform well even with...

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and populat...

In this paper, a hybrid TS-DE algorithm based on Tabu search and differential evolution algorithm is proposed to solve the reliability redundancy optimization problem. A differential evolution algorithm is embedded in Tabu search algorithm. TS is applied for searching solutions space, and DE is used for generating neighborhood solutions. The advantages of both algorithms are considered simultan...

This paper presents a dynamic optimization approach based on the differential evolution (DE) strategy which is applied to the concurrent optimal design of a continuously variable transmission (CVT). The structure-control integration approach is used to state the concurrent optimal design as a dynamic optimization problem which is solved using the Constraint Handling Differential Evolution (CHDE...