Research Article
A Multi-objective Artificial Flora Optimization Algorithm
@INPROCEEDINGS{10.1007/978-3-030-32216-8_26, author={Xuehan Wu and Huaizong Shao and Shafei Wang and Wenqin Wang}, title={A Multi-objective Artificial Flora Optimization Algorithm}, proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings}, proceedings_a={SIMUTOOLS}, year={2019}, month={10}, keywords={Swarm intelligence Artificial flora (AF) optimization algorithm Multi-objective optimization}, doi={10.1007/978-3-030-32216-8_26} }- Xuehan Wu
Huaizong Shao
Shafei Wang
Wenqin Wang
Year: 2019
A Multi-objective Artificial Flora Optimization Algorithm
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-32216-8_26
Abstract
Most of the practical problems need to consider many aspects at the same time, multi-objective optimization can be used to deal with this kind of problems. Swarm intelligence optimization algorithm can use a simple evolutionary step to find the optimal solution. Due to the advantages of swarm intelligence optimization algorithm, many researchers focus on multi-objective swarm intelligence optimization algorithms. Artificial flora (AF) optimization algorithm is a recently proposed swarm intelligence optimization algorithm. This paper proposes a multi-objective artificial flora (MOAF) optimization algorithm based on the standard artificial flora (AF) optimization algorithm. The algorithm uses the four basic elements and three main behavior patterns of the migration process and adds external document to find the Pareto optimal solution set. Simulation results show that the proposed algorithm can cover the true Pareto front with satisfactory convergence compared with the NSGA-II.
