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DOI: 10.14569/IJARAI.2015.041208
PDF

Differential Evolution Enhanced with Eager Random Search for Solving Real-Parameter Optimization Problems

Author 1: Miguel Leon
Author 2: Ning Xiong

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 4 Issue 12, 2015.

  • Abstract and Keywords
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Abstract: Differential evolution (DE) presents a class of evo-lutionary computing techniques that appear effective to handle real parameter optimization tasks in many practical applications. However, the performance of DE is not always perfect to ensure fast convergence to the global optimum. It can easily get stagnation resulting in low precision of acquired results or even failure. This paper proposes a new memetic DE algorithm by incorporating Eager Random Search (ERS) to enhance the performance of a basic DE algorithm. ERS is a local search method that is eager to replace the current solution by a better candidate in the neighborhood. Three concrete local search strategies for ERS are further introduced and discussed, leading to variants of the proposed memetic DE algorithm. In addition, only a small subset of randomly selected variables is used in each step of the local search for randomly deciding the next trial solution. The results of tests on a set of benchmark problems have demonstrated that the hybridization of DE with Eager Random Search can substantially augment DE algorithms to find better or more precise solutions while not requiring extra computing resources.

Keywords: Evolutionary Algorithm, Differential Evolution, Ea-ger Random Search, Memetic Algorithm, Optimization

Miguel Leon and Ning Xiong, “Differential Evolution Enhanced with Eager Random Search for Solving Real-Parameter Optimization Problems” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(12), 2015. http://dx.doi.org/10.14569/IJARAI.2015.041208

@article{Leon2015,
title = {Differential Evolution Enhanced with Eager Random Search for Solving Real-Parameter Optimization Problems},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2015.041208},
url = {http://dx.doi.org/10.14569/IJARAI.2015.041208},
year = {2015},
publisher = {The Science and Information Organization},
volume = {4},
number = {12},
author = {Miguel Leon and Ning Xiong}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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