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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Journal ArticleDOI
A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms.
TL;DR: A systematic, comprehensive evaluation of a large number of MOEAs that covers a wide range of experimental scenarios and confirms some of the assumed knowledge in the field, while at the same time providing new insights on the relative performance ofMOEAs for many-objective problems.
Journal ArticleDOI
A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm
TL;DR: A two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality.
Journal ArticleDOI
IT-CEMOP: An iterative co-evolutionary algorithm for multiobjective optimization problem with nonlinear constraints
TL;DR: A new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints, which maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of @e-dominance.
Proceedings ArticleDOI
An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance
TL;DR: The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level.
Journal ArticleDOI
A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction
Tobias Buer,Herbert Kopfer +1 more
TL;DR: In this article, a metaheuristic approach for the 2WDP-SC is presented, which integrates the greedy randomized adaptive search procedure with a two-stage candidate component selection procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions.