Multi-objective Cuckoo Search Algorithm

Journal of System Simulation ›› 2015, Vol. 27 ›› Issue (4): 731-737.

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Multi-objective Cuckoo Search Algorithm

He Xingshi¹, Li Na¹, Yang Xinshe^1,2, Yu Bing^1,3

1. School of Science, Xi'an Polytechnic University, Xi'an 710048, China;
2. School of Science & Technology, Middlesex University, London NW4 4BT, UK;
3. Shanghai Baosight Software Corporation, Shanghai 201300, China

Received:2014-02-24 Revised:2014-06-12 Published:2020-08-20

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.

Key words: multi-objective algorithms, cuckoo search algorithm, gradual archive reduction method based on niche technology, multi-objective cuckoo search algorithm, pareto optimal solutions

CLC Number:

TP18

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

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Multi-objective Cuckoo Search Algorithm

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