A pareto-based differential evolution algorithm for multi-objective optimization problems

Author

Lei, Ruhai ; Cheng, Yuhu

Author_Institution

Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China

fYear

2010

fDate

26-28 May 2010

Firstpage

1608

Lastpage

1613

Abstract

A new Pareto-based differential evolution (PDE) algorithm for solving multi-objective optimization problems was proposed by applying the nondominated sorting and ranking selection procedure developed in NSGA-II to select nondominated individuals to constitute a nondominated solution set. The PDE algorithm was validated using eight benchmark cases. The experimental results show that PDE, compared with NSGA-II algorithm, can find many Pareto optimal solutions distributed onto the Pareto front uniformly, which is an effective method to solve multi-objective optimization problems.

Keywords

Pareto optimisation; evolutionary computation; partial differential equations; NSGA-II algorithm; Pareto front uniformly; Pareto optimal solutions; Pareto-based differential evolution algorithm; multiobjective optimization problems; nondominated sorting; ranking selection procedure; Artificial intelligence; Electronic mail; Evolutionary computation; Genetic algorithms; Machine learning; Operations research; Optimization methods; Pareto optimization; Particle swarm optimization; Sorting; NSGA-II; Pareto; differential evolution; multi-objective optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498305

Filename

5498305