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Self-adaptive Ensemble Differential Evolution with Sampled Parameter Values for Unit Commitment

  • Nandar Lynn
  • Rammohan Mallipeddi
  • Ponnuthurai Nagaratnam SuganthanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)

Abstract

In literature, empirically and theoretically, it has been well-demonstrated that the performance of differential evolution (DE) is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. According to the No Free Lunch theorem, a single set of well-tuned combination of strategies and their associated parameter combination is not suitable for optimization problems having different characteristics. In addition, different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. Based on this observation, DE with an ensemble of mutation and crossover strategies and their associated control parameters referred to as EPSDE was proposed. However, it has been observed that the fixed discrete parameter values as in EPSDE may not yield optimal performance. In this paper, we propose self-adaptive DE algorithm (Sa-EPSDE) with a set of mutation strategies while their associated parameter values F and CR are sampled using mean and standard deviation values. In addition, the probability of selecting a combination to produce an offspring at a particular generation during the evolution process depends on the success of the combination. The performance of the proposed Sa-EPSDE algorithm is evaluated on a set of 14 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005. In order to validate the performance of proposed Sa-EPSDE algorithm on real-world applications, the algorithm is hybridized with a simple priority listing method and applied to solve unit commitment problem by considering 10-, 20-, 40-, 60-, 80- and 100-bus systems for one day scheduling period. The results showed that the proposed method obtained superior performance against other compared algorithms.

Keywords

Differential evolution Global optimization Parameter adaptation Ensemble Mutation strategy adaptation Unit commitment Scheduling 

Notes

Acknowledgement

This work was supported by the Singapore National Research Foundation (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and Cambridge Advanced Research Centre in Energy Efficiency in Singapore (CARES), C4T project.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nandar Lynn
    • 1
  • Rammohan Mallipeddi
    • 2
  • Ponnuthurai Nagaratnam Suganthan
    • 1
    Email author
  1. 1.School of Electrical and Electronics EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.School of Electronics EngineeringKyungpook National UniversityDaeguSouth Korea

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