TY - GEN
T1 - A memetic cooperative co-evolution model for large scale continuous optimization
AU - Sun, Yuan
AU - Kirley, Michael
AU - Halgamuge, Saman K.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.
AB - Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dimensional problem into sub-components that are optimized separately. However, the traditional CC framework typically employs only one EA to solve all the sub-components, which may be ineffective. In this paper, we propose a new memetic cooperative co-evolution (MCC) framework which divides a high dimensional problem into several separable and non-separable sub-components based on the underlying structure of variable interactions. Then, different local search methods are employed to enhance the search of an EA to solve the separable and non-separable sub-components. The proposed MCC model was evaluated on two benchmark sets with 35 benchmark problems. The experimental results confirmed the effectiveness of our proposed model, when compared against two traditional CC algorithms and a state-of-the-art memetic algorithm.
KW - Continuous optimization problem
KW - Cooperative co-evolution
KW - Large scale global optimization
KW - Memetic algorithm
UR - http://www.scopus.com/inward/record.url?scp=85011385259&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-51691-2_25
DO - 10.1007/978-3-319-51691-2_25
M3 - Conference contribution
SN - 9783319516905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 291
EP - 300
BT - Artificial Life and Computational Intelligence - 3rd Australasian Conference, ACALCI 2017, Proceedings
A2 - Li, Xiaodong
A2 - Wagner, Markus
A2 - Hendtlass, Tim
PB - Springer Verlag
T2 - 3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
Y2 - 31 January 2017 through 2 February 2017
ER -