TY - GEN
T1 - Opposition-based differential evolution for optimization of noisy problems
AU - Rahnamayan, Shahryar
AU - Tizhoosh, Hamid R.
AU - Salama, Magdy M.A.
PY - 2006
Y1 - 2006
N2 - Differential Evolution (DE) is a simple, reliable, and efficient optimization algorithm. However, it suffers from a weakness, losing the efficiency over optimization of noisy problems. In many real-world optimization problems we are faced with noisy environments. This paper presents a new algorithm to improve the efficiency of DE to cope with noisy optimization problems. It employs opposition-based learning for population initialization, generation jumping, and also improving population's best member. A set of commonly used benchmark functions is employed for experimental verification. The details of proposed algorithm and also conducted experiments are given. The new algorithm outperforms DE in terms of convergence speed.
AB - Differential Evolution (DE) is a simple, reliable, and efficient optimization algorithm. However, it suffers from a weakness, losing the efficiency over optimization of noisy problems. In many real-world optimization problems we are faced with noisy environments. This paper presents a new algorithm to improve the efficiency of DE to cope with noisy optimization problems. It employs opposition-based learning for population initialization, generation jumping, and also improving population's best member. A set of commonly used benchmark functions is employed for experimental verification. The details of proposed algorithm and also conducted experiments are given. The new algorithm outperforms DE in terms of convergence speed.
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M3 - Conference contribution
AN - SCOPUS:34547240097
SN - 0780394879
SN - 9780780394872
T3 - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
SP - 1865
EP - 1872
BT - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
T2 - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
Y2 - 16 July 2006 through 21 July 2006
ER -