TY - GEN
T1 - Improved adaptive differential evolution algorithm with external archive
AU - Mallipeddi, Rammohan
AU - Suganthan, Ponnuthurai Nagaratnam
PY - 2013
Y1 - 2013
N2 - Depending on the complexity of the optimization problem, the performance of differential evolution (DE) algorithm is quite sensitive to the choice of mutation and crossover strategies and their associated control parameters. To obtain optimal performance, while avoiding time consuming parameter tuning, different adaptive and self-adaptive techniques that can update the strategies and/or the parameters during the evolution have been proposed. Adaptive differential evolution with optional archive (JADE) is one of the popular adaptive algorithms that perform well on most of the optimization problems. Motivated by the performance of the JADE algorithm, this paper presents an improved adaptive differential evolution algorithm with external archive (iJADE). Unlike the optional archive in JADE, iJADE algorithm employs an external archive which is updated every generation by tournament selection to incorporate the parents which cannot progress to the next generation. In addition, iJADE uses an ensemble of two crossover strategies, binomial and exponential, instead of a single crossover strategy as in JADE. The performance of the algorithm is evaluated on a set of 16 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with JADE algorithm.
AB - Depending on the complexity of the optimization problem, the performance of differential evolution (DE) algorithm is quite sensitive to the choice of mutation and crossover strategies and their associated control parameters. To obtain optimal performance, while avoiding time consuming parameter tuning, different adaptive and self-adaptive techniques that can update the strategies and/or the parameters during the evolution have been proposed. Adaptive differential evolution with optional archive (JADE) is one of the popular adaptive algorithms that perform well on most of the optimization problems. Motivated by the performance of the JADE algorithm, this paper presents an improved adaptive differential evolution algorithm with external archive (iJADE). Unlike the optional archive in JADE, iJADE algorithm employs an external archive which is updated every generation by tournament selection to incorporate the parents which cannot progress to the next generation. In addition, iJADE uses an ensemble of two crossover strategies, binomial and exponential, instead of a single crossover strategy as in JADE. The performance of the algorithm is evaluated on a set of 16 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with JADE algorithm.
KW - Differential Evolution
KW - External archive
KW - Global optimization
KW - Parameter adaptation
UR - https://www.scopus.com/pages/publications/84893232036
U2 - 10.1007/978-3-319-03753-0_16
DO - 10.1007/978-3-319-03753-0_16
M3 - Conference contribution
AN - SCOPUS:84893232036
SN - 9783319037523
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 178
BT - Swarm, Evolutionary, and Memetic Computing - 4th International Conference, SEMCCO 2013, Proceedings
T2 - 4th International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2013
Y2 - 19 December 2013 through 21 December 2013
ER -