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Carlos A. Coello Coello
Researcher at CINVESTAV
Publications - 637
Citations - 41634
Carlos A. Coello Coello is an academic researcher from CINVESTAV. The author has contributed to research in topics: Evolutionary algorithm & Multi-objective optimization. The author has an hindex of 83, co-authored 601 publications receiving 36469 citations. Previous affiliations of Carlos A. Coello Coello include Tulane University & Instituto Politécnico Nacional.
Papers
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Journal ArticleDOI
Handling multiple objectives with particle swarm optimization
TL;DR: An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
Journal ArticleDOI
Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art
TL;DR: A comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms, including approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies.
Proceedings ArticleDOI
MOPSO: a proposal for multiple objective particle swarm optimization
TL;DR: This paper introduces a proposal to extend the heuristic called "particle swarm optimization" (PSO) to deal with multiobjective optimization problems and it maintains previously found nondominated vectors in a global repository that is later used by other particles to guide their own flight.
Book
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Journal ArticleDOI
A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques
TL;DR: A critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms.