Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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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
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Proceedings ArticleDOI
Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods
Dimo Brockhoff,Eckart Zitzler +1 more
TL;DR: The study indicates that the (temporary) omission of objectives can improve hypervolume based MOEAs drastically in terms of the achieved hypervolume indicator values.
Journal ArticleDOI
A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment
TL;DR: This work proposes the novel approach of tracking and predicting the changes in the location of the Pareto Set in order to minimize the effects of a landscape change and incorporated into a variant of the multi-objective evolutionary gradient search (MO-EGS), and two other MOEAs for dynamic optimization.
Proceedings ArticleDOI
Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey
L. Rachmawati,Dipti Srinivasan +1 more
TL;DR: The incorporation of preference in Evolutionary Multi-objective Optimization (EMO) promotes better decisionmaking and increases the specificity of selection, leading to solutions which are of higher relevance to the Decision Maker(s).
Proceedings ArticleDOI
Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems
Saku Kukkonen,Kalyanmoy Deb +1 more
TL;DR: It is revealed that crowding distance does not estimate crowdedness well in this case and presumably also in cases of more objectives, so the proposed pruning algorithm cannot be used for tri-objective test problems.
Book ChapterDOI
2.2 Nine Considerations for Constructing and Running Geomorphological Models
TL;DR: In this paper, three broad categories of geomorphological models are considered: (1) traditional physically based computer models; (2) cellular-automata models; and (3) statistical models of observations or simulated data.