A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

作者：Zhang, Hu; Song, Shenmin^*; Zhou, Aimin; Gao, X. Z.

来源：International Journal of Machine Learning and Cybernetics, 2015, 6(3): 487-500.

DOI：10.1007/s13042-014-0277-6

• Multiobjective optimization
• Cellular genetic algorithm
• 3D grid
• Crowding measurement

Multiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosine crowding measurement. The first component is used to organize the population. Compared with a 2D grid, the 3D grid offers a vertical expansion of cells. The second one simultaneously considers the crowding distances and location distributions for measuring the crowding degree values for the solutions. The simulation results show that C-MCGA outperforms two typical MOcGAs and two state-of-the-art algorithms, NSGA-II and SPEA2, on a given set of test instances. Furthermore, the proposed measurement metric is compared with that in NSGA-II, which is demonstrated to yield a more diverse population on most of the test instances.

• 出版日期2015-6
• 单位哈尔滨工业大学; 华东师范大学