A Variant of Competitive Differential Evolution Algorithm with Exponential Crossover

Summary

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The differential evolution (DE) algorithm is a powerful population-based stochastic technique to search for global optimum in the continuous search space. Success of DE algorithm strongly depends on choosing its parameters. The competition in differential evolution was shown to be an efficient instrument to avoid time-consuming process of tuning control parameters. A new variant of competitive DE algorithm, called BEBERAN, was proposed and tested on benchmark functions at four levels of the search space dimension. The BEBERAN was compared with the most promising competitive variant, DEBR18. BEBERAN, in contrast to DEBR18, includes in addition the exponential crossover.

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A Variant of Competitive Differential Evolution Algorithm with Exponential Crossover

(ProQuest: ... denotes formulae omitted.)

1. Introduction

We address the global optimization problem: for a given objective function f : D [arrow right] R, D ⊂ R^sup d^ we are searching a point x* , which is called the global minimum point when satisfies

x* ∈ {a ∈ D; a = argmin^sub x∈D^ f(x)}. (1)

The search space D is a closed compact set D = Π^sup d^^sub i=1^[a^sub i^,b^sub i^]; a^sub i^ < b^sub i^, i = 1, 2, . . . , d, the objective function / is continuous and f(x) can be evaluated at any point x ∈ D. The global optimization problem is difficult one. There are many different numerous stochastic algorithms which were proposed for solving the global optimization problem. Differential evo...

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