Proceedings ArticleDOI
Use of cooperative coevolution for solving large scale multiobjective optimization problems
Luis Miguel Antonio,Carlos A. Coello Coello +1 more
- pp 2758-2765
TLDR
This paper proposes a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems and compares its proposed algorithm with respect to two state-of-the-art multi- objective evolutionary algorithms.Abstract:
Many real-world multi-objective optimization problems have hundreds or even thousands of decision variables, which contrast with the current practice of multi-objective metaheuristics whose performance is typically assessed using benchmark problems with a relatively low number of decision variables (normally, no more than 30). In this paper, we propose a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems. We adopt a benchmark that is scalable in the number of decision variables (the ZDT test suite) and compare our proposed algorithm with respect to two state-of-the-art multi-objective evolutionary algorithms (GDE3 and NSGA-II) when using a large number of decision variables (from 200 up to 5000). The results clearly indicate that our proposed approach is effective as well as efficient for solving large scale multi-objective optimization problems.read more
Citations
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Journal ArticleDOI
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
Yuan Yuan,Hua Xu,Bo Wang,Xin Yao +3 more
TL;DR: In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization that aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition.
Journal ArticleDOI
Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
Journal ArticleDOI
Metaheuristics in large-scale global continues optimization
TL;DR: The paper mainly covers the fundamental algorithmic frameworks such as decomposition and non-decomposition methods, and their current applications in the field of large-scale global optimization.
Journal ArticleDOI
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization
TL;DR: The experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art evolutionary algorithms in terms of the scalability to decision variables on MaOPs.
Journal ArticleDOI
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization
TL;DR: The proposed improved variant of the differential grouping (DG) algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors and automatic calculation of its threshold parameter, which makes it parameter-free.
References
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Proceedings Article
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Proceedings Article
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
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