A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization

被引:46
作者
Dhiman, Gaurav [1 ]
Soni, Mukesh [2 ]
Pandey, Hari Mohan [3 ]
Slowik, Adam [4 ]
Kaur, Harsimran [1 ]
机构
[1] Govt Bikram Coll Commerce, Dept Comp Sci, Patiala 147001, Punjab, India
[2] Smt SR Patel Engn Coll, Dept Comp Engn, Unjha 384170, Gujarat, India
[3] Edge Hill Univ, Dept Comp Sci, Ormskirk, England
[4] Koszalin Univ Technol, Dept Elect & Comp Sci, Koszalin, Poland
关键词
Many-objective optimization; Hypervolume estimation algorithm; Reference vector guided evolutionary algorithm; Constrained optimization; Pareto optimality; SPOTTED HYENA OPTIMIZER; MULTIOBJECTIVE OPTIMIZATION; PART I;
D O I
10.1007/s00366-020-00986-0
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A novel hybrid many-objective evolutionary algorithm called Reference Vector Guided Evolutionary Algorithm based on hypervolume indicator (H-RVEA) is proposed in this paper. The reference vectors are used in a number of sub-problems to decompose the optimization problem. An adaptation strategy is used in the proposed algorithm to adjust the reference vector distribution. The proposed algorithm is compared over well-known benchmark test functions with five state-of-the-art evolutionary algorithms. The results show H-RVEA's superior performance in terms of the inverted generational distance and hypervolume performance measures than the competitor algorithms. The suggested algorithm's computational complexity is also analysed. The statistical tests are carried out to demonstrate the statistical significance of the proposed algorithm. In order to demonstrate its efficiency, H-RVEA is also applied to solve two real-life constrained many-objective optimization problems. The experimental results indicate that the proposed algorithm can solve the many-objective real-life problems. Note that the source codes of the proposed technique are available at .
引用
收藏
页码:3017 / 3035
页数:19
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