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Improving differential evolution through a unified approach

Author

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  • Nikhil Padhye
  • Piyush Bhardawaj
  • Kalyanmoy Deb

Abstract

Only a few attempts in past have been made in adopting a unified outlook towards different paradigms in evolutionary computation (EC). The underlying motivation of these studies was aimed at gaining better understanding of evolutionary methods, both at the level of theory as well as application, in order to design efficient evolutionary algorithms for solving wide-range of complex problems. However, the past descriptions have either been too general or sometimes abstract in issuing a clear direction for improving an evolutionary paradigm for a task-specific. This paper recollects the ‘Unified Theory of Evolutionary Computation’ from past and investigates four steps—Initialization, Selection, Generation and Replacement, which are sufficient to describe traditional forms of Evolutionary Optimization Systems such as Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming, Particle Swarm Optimization and differential evolution (DE). Then, a relatively new evolutionary paradigm, DE, is chosen and studied for its performance on a set of unimodal problems. Discovering DEs inability as an efficient solver, DE is reviewed under ‘Unified Framework’ and functional requirements of each step are evaluated. Targeted towards enhancing the DE’s performance, several modifications are proposed through borrowing of operations from a benchmark solver G3-PCX. Success of this exercise is demonstrated in a step-by-step fashion via simulation results. The Unified Approach is highly helpful in understanding the role and re-modeling of DE steps in order to efficiently solve unimodal problems. In an avalanching-age of new methods in EC, this study outlines a direction for advancing EC methods by undertaking a collective outlook and an approach of concept-sharing. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Nikhil Padhye & Piyush Bhardawaj & Kalyanmoy Deb, 2013. "Improving differential evolution through a unified approach," Journal of Global Optimization, Springer, vol. 55(4), pages 771-799, April.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:4:p:771-799
    DOI: 10.1007/s10898-012-9897-0
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    Citations

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    Cited by:

    1. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    2. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    3. Nikhil Padhye & Pulkit Mittal & Kalyanmoy Deb, 2015. "Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization," Computational Optimization and Applications, Springer, vol. 62(3), pages 851-890, December.
    4. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    5. Thang Trung Nguyen & Nguyen Vu Quynh & Minh Quan Duong & Le Van Dai, 2018. "Modified Differential Evolution Algorithm: A Novel Approach to Optimize the Operation of Hydrothermal Power Systems while Considering the Different Constraints and Valve Point Loading Effects," Energies, MDPI, vol. 11(3), pages 1-30, March.

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