A novel hybrid differential evolution algorithm with modified CoDE and JADE

doi:10.1016/j.asoc.2016.06.011

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A novel hybrid differential evolution algorithm with modified CoDE and JADE

2016-10-01

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DOI：10.1016/j.asoc.2016.06.011

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Abstract

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JADE and CoDE are two well-known state-of-the-art DE algorithms for solving global optimization problems (GOPs). JADE is found to be suitable for solving unimodal and simple multimodal functions as an exploitation mutation strategy, i.e.DE/current-to-pbest/1, is employed, while CoDE is shown to fit for handling complicated multimodal functions due to its exploration mutation strategies, such as DE/rand/1 /bin, DE/currant-to-rand/1, and DE/rand/2/bin. To combine their merits for tackling different types of GOPs, a novel hybrid framework is designed based on the modified JADE (MJADE) and modified CoDE (MCoDE), named HMJCDE. Different from the simple combination of MJADE and MCoDE, they are operated alternatively according to the improvement rate of the fitness value. To assess the performance of HMJCDE, 30 benchmark problems taken from CEC2014 competition on real parameter optimization are employed. When compared with JADE, CoDE, other state-of-the-art DE variants and non-DE heuristic algorithms, HMJCDE performs better than all the competitors on most of test problems. Moreover, the sensitivity analysis of some parameters in HMJCDE is conducted and the effectiveness of our proposed hybrid framework is also justified experimentally. (C) 2016 Elsevier B.V. All rights reserved.

Keywords:

Differential evolution

Modified JADE

Modified CoDE

Hybrid framework

Global numerical optimization

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