A Multi-Objective Evolutionary Algorithm based on Length Reduction for Large-Scale Instance Selection

2021 ◽  
Author(s):  
Fan Cheng ◽  
Feixiang Chu ◽  
Lei Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Instance Selection ◽  
Multi Objective ◽  
Length Reduction

Combining exhaustive search and multi-objective evolutionary algorithm for service restoration in large-scale distribution systems

2016 ◽  
Vol 134 ◽  
pp. 1-8 ◽  
Author(s):  
Marcos H.M. Camillo ◽  
Rodrigo Z. Fanucchi ◽  
Marcel E.V. Romero ◽  
Telma Woerle de Lima ◽  
Anderson da Silva Soares ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Distribution Systems ◽  
Large Scale ◽  
Exhaustive Search ◽  
Multi Objective ◽  
Service Restoration

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

Biology ◽  
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.

Fast Evolutionary Algorithm for Solving Large-Scale Multi-objective Problems

2020 ◽  
pp. 82-95
Author(s):  
Anna Ouskova Leonteva ◽  
Pierre Parrend ◽  
Anne Jeannin-Girardon ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Multi Objective

Multi-Objective Evolutionary Algorithm for single and multiple fault service restoration in large-scale distribution systems

2014 ◽  
Vol 110 ◽  
pp. 144-153 ◽  
Author(s):  
Danilo Sipoli Sanches ◽  
João Bosco A. London Junior ◽  
Alexandre Cláudio B. Delbem
Keyword(s):  
Evolutionary Algorithm ◽  
Distribution Systems ◽  
Large Scale ◽  
Multiple Fault ◽  
Multi Objective ◽  
Service Restoration

scholarly journals A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

2022 ◽  
Author(s):  
Federico Antonello ◽  
Piero Baraldi ◽  
Enrico Zio ◽  
Luigi Serio
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
State Of The Art ◽  
Solution Space ◽  
Nuclear Research ◽  
Functional Dependencies ◽  
Direct Identification ◽  
European Organization ◽  
Local Optima ◽  
Multi Objective

AbstractIn this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Multi-Objective Evolutionary Algorithm with Node-Depth Encoding and Strength Pareto for Service Restoration in Large-Scale Distribution Systems

2013 ◽  
pp. 771-786 ◽  
Author(s):  
Marcilyanne Moreira Gois ◽  
Danilo Sipoli Sanches ◽  
Jean Martins ◽  
João Bosco A. London Junior ◽  
Alexandre Cláudio Botazzo Delbem
Keyword(s):  
Evolutionary Algorithm ◽  
Distribution Systems ◽  
Large Scale ◽  
Multi Objective ◽  
Service Restoration ◽  
Node Depth
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