Multi-objective Cuckoo Search Algorithm

Authors

Xingshi He, 1. School of Science, Xi'an Polytechnic University, Xi'an 710048, China;;
Li Na, 1. School of Science, Xi'an Polytechnic University, Xi'an 710048, China;;
Xinshe Yang, 1. School of Science, Xi'an Polytechnic University, Xi'an 710048, China;;2. School of Science & Technology, Middlesex University, London NW4 4BT, UK;;
Yu Bing, 1. School of Science, Xi'an Polytechnic University, Xi'an 710048, China;;3. Shanghai Baosight Software Corporation, Shanghai 201300, China;

Abstract

Abstract: It is challenging to solve multi-objective optimization problems with getting high-quality Pareto fronts accurately. The multi-objective Cuckoo Search algorithm (MOCS) was designed by firstly applying the recently developed Cuckoo Search Algorithm (CS) in solving Multi-objective optimization problems, and the fitness function based Pareto definiteness was improved, and the Gradual archive reduction method based on niche technology was proposed to improve the Archive solutions quality. The simulation test results and related performance indicators of nine test problems show that, MOCS algorithm is obviously improved in the aspect of the convergence, the diversity and the uniformity compared with the classic NSGA-II algorithm.

Recommended Citation

He, Xingshi; Na, Li; Yang, Xinshe; and Bing, Yu (2020) "Multi-objective Cuckoo Search Algorithm," Journal of System Simulation: Vol. 27: Iss. 4, Article 9.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss4/9

First Page

731

Revised Date

2014-06-12

DOI Link

https://doi.org/

Last Page

737

CLC

TP18

Recommended Citation

He Xingshi, Li Na, Yang Xinshe, Yu Bing. Multi-objective Cuckoo Search Algorithm[J]. Journal of System Simulation, 2015, 27(4): 731-737.