TY - GEN
T1 - Non-dominated sorting and crowding distance based multi-objective chaotic evolution
AU - Pei, Yan
AU - Hao, Jia
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - We propose a new evolutionary multi-objective optimization (EMO) algorithm based on chaotic evolution optimization framework, which is called as multi-objective chaotic evolution (MOCE). It extends the optimization application of chaotic evolution algorithm to multi-objective optimization field. The non-dominated sorting and tournament selection using crowding distance are two techniques to ensure Pareto dominance and solution diversity in EMO algorithm. However, the search capability of multi-objective optimization algorithm is a serious issue for its practical application. Chaotic evolution algorithm presents a strong search capability for single objective optimization due to the ergodicity of chaotic system. Proposed algorithm is a promising multi-objective optimization algorithm that composes a search algorithm with strong search capability, dominant sort for keeping Pareto dominance, and tournament selection using crowding distance for increasing the solution diversity. We evaluate our proposed MOCE by comparing with NSGA-II and an algorithm using the basic framework of chaotic evolution but different mutation strategy. From the evaluation results, the MOCE presents a strong optimization performance for multi-objective optimization problems, especially in the condition of higher dimensional problems. We also analyse, discuss, and present some research subjects, open topics, and future works on the MOCE.
AB - We propose a new evolutionary multi-objective optimization (EMO) algorithm based on chaotic evolution optimization framework, which is called as multi-objective chaotic evolution (MOCE). It extends the optimization application of chaotic evolution algorithm to multi-objective optimization field. The non-dominated sorting and tournament selection using crowding distance are two techniques to ensure Pareto dominance and solution diversity in EMO algorithm. However, the search capability of multi-objective optimization algorithm is a serious issue for its practical application. Chaotic evolution algorithm presents a strong search capability for single objective optimization due to the ergodicity of chaotic system. Proposed algorithm is a promising multi-objective optimization algorithm that composes a search algorithm with strong search capability, dominant sort for keeping Pareto dominance, and tournament selection using crowding distance for increasing the solution diversity. We evaluate our proposed MOCE by comparing with NSGA-II and an algorithm using the basic framework of chaotic evolution but different mutation strategy. From the evaluation results, the MOCE presents a strong optimization performance for multi-objective optimization problems, especially in the condition of higher dimensional problems. We also analyse, discuss, and present some research subjects, open topics, and future works on the MOCE.
KW - Chaos theory
KW - Chaotic evolution
KW - Chaotic optimization
KW - Evolutionary multi-objective optimization
KW - Multi-objective chaotic evolution
UR - https://www.scopus.com/pages/publications/85026724533
U2 - 10.1007/978-3-319-61833-3_2
DO - 10.1007/978-3-319-61833-3_2
M3 - Conference contribution
AN - SCOPUS:85026724533
SN - 9783319618326
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 22
BT - Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
A2 - Niu, Ben
A2 - Takagi, Hideyuki
A2 - Shi, Yuhui
A2 - Tan, Ying
PB - Springer Verlag
T2 - 8th International Conference on Swarm Intelligence, ICSI 2017
Y2 - 27 July 2017 through 1 August 2017
ER -