Regular Pareto Front Shape is not Realistic

Ishibuchi, Hisao; He, Linjun; Shang, Ke

10.1109/CEC.2019.8790342

2019

978-1-7281-2154-3

2019 IEEE Congress on Evolutionary Computation, CEC 2019

2034-2041

10-13 June 2019

Wellington, New zealand

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

Performance of evolutionary multi-objective and many-objective optimization algorithms is usually evaluated by computational experiments on a number of test problems. Thus, performance comparison results depend on the choice of test problems. For fair comparison, it is needed to use a wide variety of test problems with various characteristics. However, most of well-known and frequently-used scalable test problems have the same type of Pareto fronts called "regular" Pareto fronts: Their shape is triangular. In this paper, we discuss the reality of this type of Pareto fronts. First, we show that a triangular Pareto front has some unrealistic properties as the Pareto front of a real-world multi-objective problem. Next, we examine the shape of the Pareto fronts of some other multi-objective test problems with independently generated objectives (i.e., with objectives that are not derived from a pre-specified shape of Pareto fronts). It is shown that the Pareto fronts of those test problems are inverted triangular (i.e., not regular). Then, we demonstrate that the shape of Pareto fronts (i.e., triangular or inverted triangular) has large effects on the performance of decomposition-based and hypervolume-based algorithms. Finally, we show difficulties of hypervolume-based performance evaluation for many-objective problems with inverted triangular Pareto fronts.
© 2019 IEEE.

Evolutionary multi-objective optimization regular Pareto fronts irregular Pareto fronts triangular Pareto fronts inverted triangular Pareto fronts

第一 ; 通讯

英语

EI ; CPCI-S

National Natural Science Foundation of China[61876075]

Engineering ; Mathematical & Computational Biology

Engineering, Electrical & Electronic ; Mathematical & Computational Biology

WOS:000502087102007

20193507373660

Evolutionary algorithms

Optimization Techniques:921.5

EV Compendex

Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Southern University of Science and Technology (SUSTech), Shenzhen, China

Ishibuchi, Hisao,He, Linjun,Shang, Ke. Regular Pareto Front Shape is not Realistic[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2034-2041.