Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
About:
The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
Citations
More filters
Book ChapterDOI
Fitting Hydrological Models on Multiple Responses Using the Multiobjective Evolutionary Annealing-Simplex Approach
TL;DR: The multiobjective evolutionary annealing-simplex (MEAS) method implements an innovative scheme, particularly developed for the optimization of such problems, dealing with hydrological modeling and water resource management in a karstic basin in Greece.
Journal ArticleDOI
Etea: A euclidean minimum spanning tree-based evolutionary algorithm for multi-objective optimization
TL;DR: From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.
Posted Content
Optimal Placement of Conservation Practices Using Genetic Algorithm with SWAT
TL;DR: In this paper, the authors combine the tools of evolutionary algorithm with the Soil and Water Assessment Tool (SWAT) model and cost data to develop a trade-off frontier of least cost of achieving nutrient reductions and the corresponding locations of conservation practices.
Journal ArticleDOI
An improved multiobjective estimation of distribution algorithm for environmental economic dispatch of hydrothermal power systems
TL;DR: An improved multiobjective estimation of distribution algorithm (IRM-MEDA) is used to solve the environmental economic dispatch of hydrothermal power systems and the superiority of this proposed method as a promising MOEA as compared with other three MOEAs is compared.
Journal ArticleDOI
Multi-objective multiagent credit assignment in reinforcement learning and NSGA-II
Logan Yliniemi,Kagan Tumer +1 more
TL;DR: This work derives multiple methods for incorporating difference evaluations into a state-of-the-art multi-objective evolutionary algorithm, NSGA-II, and suggests that in a multiagent multi-Objective problem, proper credit assignment is at least as important to performance as the choice of multi- objective algorithm.