Multi-agent genetic algorithm with controllable mutation probability utilizing back propagation neural network for global optimization of trajectory design

Abstract

A Controllable Mutation Probability (CMP) strategy is proposed and applied to a Multi-Agent Genetic Algorithm (MAGA) to deal with the global optimization of trajectory design in deep space, which is called MGA-CMP. MAGA-CMP is an algorithm setting all the individuals (or agents) on a grid and having two controlling functions to adjust the performance probability of a mutation operator. It pays more attention to global search in the earlier part of the process, and devotes more effort to local search at later stages. Four experiments are implemented to illustrate the efficiency of MAGA-CMP, and results show that MGA-CMP performs better in most examined cases than other well-known search algorithms.

Keywords:

• global optimization
• trajectory design
• MGA
• MAGA-CMP

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant no. 61472375]; the 13th Five-year Pre-research Project of Civil Aerospace in China, Joint Funds of the Equipment Pre-Research and Ministry of Education of China [grant no. 6141A02022320]; and the Fundamental Research Funds for the Central Universities [grant nos. CUG2017G01 and CUG160207].