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Real-Parameter Unconstrained Optimization Based on Enhanced AGDE Algorithm

  • Ali Khater Mohamed
  • Ali Wagdy Mohamed
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

Adaptive guided differential evolution algorithm (AGDE) is a differential evolution (DE) algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population into three clusters (best, better and worst) with sizes 100p%, NP − 2 * 100% and 100% respectively. where p is the proportion of the partition with respect to the total number of individuals in the population (NP). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems. The performance of the proposed algorithm is evaluated using CEC2013 benchmarks and the results are compared with the state-of-art DE and non-DE algorithms, the results showed a great competitiveness for the proposed algorithm over the other algorithms, and the original AGDE.

Keywords

Differential evolution Novel mutation Adaptive crossover Initial population Population reduction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Business Administration, College of Sciences and HumanitiesMajmaah UniversityMajmaahSaudi Arabia
  2. 2.Operations Research DepartmentInstitute of Statistical Studies and Research, Cairo UniversityGizaEgypt

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