TY - GEN

T1 - Micro-differential evolution with vectorized random mutation factor

AU - Salehinejad, Hojjat

AU - Rahnamayan, Shahryar

AU - Tizhoosh, Hamid R.

AU - Chen, Stephen Y.

N1 - Publisher Copyright:
© 2014 IEEE.

PY - 2014/9/16

Y1 - 2014/9/16

N2 - One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a microdifferential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.

AB - One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a microdifferential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.

UR - http://www.scopus.com/inward/record.url?scp=84908577383&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908577383&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900606

DO - 10.1109/CEC.2014.6900606

M3 - Conference contribution

AN - SCOPUS:84908577383

T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

SP - 2055

EP - 2062

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -