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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.

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Dissertation

Surrogate-Assisted Evolutionary Algorithms

TL;DR: L'algorithme ainsi defini, saACM-ES, integre etroitement l'optimisation realisee par CMA-ES et l'apprentissage statistique de meta-modeles adaptatifs; en particulier les meta- modeles reposent sur the matrice de covariance adaptee par C MA-ES.
Journal ArticleDOI

Tri-Goal Evolution Framework for Constrained Many-Objective Optimization

TL;DR: A tri-goal evolution framework is proposed for CMaOPs that carefully designs two indicators for convergence and diversity, respectively, and converts the constraints into the third indicator for feasibility, and is conceptually simple and easy to instantiate for constrained many-objective optimization.
Proceedings ArticleDOI

Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression

TL;DR: This paper proposes an adaptive method to periodically regenerate the weight vectors for decomposition-based multi-objective algorithms according to the geometry of the estimated Pareto front, and proves the effectiveness of the proposed adaptive weights generation method.
Journal ArticleDOI

QoS-Driven Service Composition with Reconfigurable Services

TL;DR: A novel compositional decision making process, CDP, which explores optimal solutions of individual component services and uses the knowledge to derive optimal QoS-driven composition solutions and can significantly reduce the search space and achieve great performance gains.
Journal ArticleDOI

Optimal Design of Gravity-Fed Looped Water Distribution Networks Considering the Resilience Index

TL;DR: In this paper, the authors evaluate the performance of several multiobjective metaheuristics (MOMHs) to optimize the design of looped water distribution networks, taking into consideration two objective functions: Minimizing costs and maximizing the resilience index.
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