Auto-Thermal Chemical Looping Reforming Process in a Network of Catalytic Packed-Bed Reactors for Large-Scale Syngas Production: A Comprehensive Dynamic Modeling and Multi-Objective Optimization

Non-dominated sorting genetic algorithms II (NSGAII) has been widely used for multi- objective optimizations. To overcome its premature shortcoming, an improved NSGAII with a new distribution was proposed in this paper. Comparative to NSGAII, improved NSGAII uses an elitist control strategy to protect its lateral diversity among current non-dominated fronts. To implement elitist control strategy, a new distribution (called dogmatic distribution) was proposed. For ordinary multi-objective optimization problem (MOP), an ordinary exploration ability of improved NSGAII should be maintained by using a larger shape parameter r; while for larger-scale complex MOP, a larger exploration ability of improved NSGAII should be maintained by using a less shape parameter r. The application of improved NSGAII in multi-objective operation of Wohu reservoir shows that improved NSGAII has advantages over NSGAII to get better Pareto front especially for large-scale complex multi-objective reservoir operation problems.

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Weighted Optimization Framework for Large-scale Multi-objective Optimization

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion ◽

10.1145/2908961.2908979 ◽

2016 ◽

Cited By ~ 7

Author(s):

Heiner Zille ◽

Hisao Ishibuchi ◽

Sanaz Mostaghim ◽

Yusuke Nojima

Keyword(s):

Large Scale ◽

Multi Objective Optimization ◽

Multi Objective ◽

Optimization Framework ◽

Weighted Optimization

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Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽

10.3390/biology10121287 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1287

Author(s):

Xingsi Xue ◽

Pei-Wei Tsai ◽

Yucheng Zhuang

Keyword(s):

Evolutionary Algorithm ◽

Large Scale ◽

Biomedical Ontology ◽

Biomedical Data ◽

Biomedical Ontologies ◽

Ontology Alignment ◽

Multi Objective Optimization ◽

Matching Problem ◽

Multi Objective ◽

Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

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