Title :
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
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;
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
DOI :
10.1109/CCDC.2010.5498305
