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
Adaptive Epsilon dominance in decomposition-based multiobjective evolutionary algorithm
TL;DR: This work proposes a new decomposition-based multiobjective evolutionary algorithm based on a hybrid weighting strategy, which optimizes both random subpro problems and fixed subproblems and maintains diversity of nondominated solutions stored in external population.
Journal ArticleDOI
Design of electronically steerable linear arrays with evolutionary algorithms
TL;DR: The main goal and contribution of this paper is computation of the trade-off curves between side lobe level and main beam width for steerable linear arrays.
Journal ArticleDOI
Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models
Edwin Lughofer,Alexandru-Ciprian Zavoianu,Robert Pollak,Mahardhika Pratama,Pauline Meyer-Heye,Helmut Zörrer,Christian Eitzinger,Thomas Radauer +7 more
TL;DR: A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time.
Proceedings ArticleDOI
A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems
TL;DR: Novel multi-objective genetic programming based hyper-heuristic methods for automatic design of SPs including dispatching rules (DRs) and due-date assignment rules (DDARs) in job shop environments and the proposed Diversified Multi-Objective Cooperative Coevolution (DMOCC) method can effectively evolve Pareto fronts.
Proceedings Article
A Parallel Plugin-Based Framework for Multi-objective Optimization.
TL;DR: The plugin-based architecture of the framework minimizes the final user effort required to incorporate their own problems and evolutionary algorithms, and facilitates the tool maintenance, and demonstrates the efficiency of the provided parallel implementation.