EDA-Based multi-objective optimization using preference order ranking and multivariate gaussian copula

Authors:
Ying Gao;Lingxi Peng;Fufang Li;Miao Liu;Xiao Hu
Affiliations:
Department of Computer Science and Technology, Guangzhou University, Guangzhou, P.R. of China;Department of Computer Science and Technology, Guangzhou University, Guangzhou, P.R. of China;Department of Computer Science and Technology, Guangzhou University, Guangzhou, P.R. of China;Department of Computer Science and Technology, Guangzhou University, Guangzhou, P.R. of China;Department of Computer Science and Technology, Guangzhou University, Guangzhou, P.R. of China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

Citing 11
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Performance assessment of generalized differential evolution 3 with a given set of constrained multi-objective test problems

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-objective optimization with artificial weed colonies

Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization

IEEE Transactions on Evolutionary Computation
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Estimation of distribution algorithms (EDAs) are a class of evolutionary optimization algorithms based on probability distribution model. This article extends the basic EDAs for tackling multi-objective optimization problems by incorporating multivariate Gaussian copulas for constructing probability distribution model, and using the concept of preference order. In the algorithm, the multivariate Gaussian copula is used to construct probability distribution model in EDAs. By estimating Kendall's τ and using the relationship of correlation matrix and Kendall's τ, correlation matrix R in Gaussian copula are firstly estimated from the current population, and then is used to generate offsprings. Preference order is used to identify the best individuals in order to guide the search process. The population with the current population and current offsprings population is sorted based on preference order, and the best individuals are selected to form the next population. The algorithm is tested to compare with NSGA-II, GDE, MOEP and MOPSO based on convergence metric and diversity metric using a set of benchmark functions. The experimental results show that the algorithm is effective on the benchmark functions.