scholarly journals Construction of Shear Wave Models by Applying Multi-Objective Optimization to Multiple Geophysical Data Sets

2015 ◽  
pp. 309-325 ◽  
Author(s):  
Lennox Thompson ◽  
Aaron A. Velasco ◽  
Vladik Kreinovich
Keyword(s):  
Shear Wave ◽  
Geophysical Data ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wave Models

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Multi-Objective Optimization-Based Networked Multi-Label Active Learning

2019 ◽  
Vol 30 (2) ◽  
pp. 1-26
Author(s):  
Lei Li ◽  
Yuqi Chu ◽  
Guanfeng Liu ◽  
Xindong Wu
Keyword(s):  
Prediction Accuracy ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Research Directions ◽  
Seed Node ◽  
Multi Objective ◽  
Network Applications ◽  
Fast Development ◽  
System Overhead

Along with the fast development of network applications, network research has attracted more and more attention, where one of the most important research directions is networked multi-label classification. Based on it, unknown labels of nodes can be inferred by known labels of nodes in the neighborhood. As both the scale and complexity of networks are increasing, the problems of previously neglected system overhead are turning more and more seriously. In this article, a novel multi-objective optimization-based networked multi-label seed node selection algorithm (named as MOSS) is proposed to improve both the prediction accuracy for unknown labels of nodes from labels of seed nodes during classification and the system overhead for mining the labels of seed nodes with third parties before classification. Compared with other algorithms on several real networked data sets, MOSS algorithm not only greatly reduces the system overhead before classification but also improves the prediction accuracy during classification.

scholarly journals Inversion of marine controlled source electromagnetic data using a parallel non-dominated sorting genetic algorithm

2019 ◽  
Vol 220 (2) ◽  
pp. 1066-1077 ◽  
Author(s):  
Mohit Ayani ◽  
Lucy MacGregor ◽  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Synthetic Data ◽  
Optimization Method ◽  
Model Parameters ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Controlled Source ◽  
Multi Objective ◽  
Electromagnetic Data ◽  
Deterministic Methods

SUMMARY We developed a multi-objective optimization method for inverting marine controlled source electromagnetic data using a fast-non-dominated sorting genetic algorithm. Deterministic methods for inverting electromagnetic data rely on selecting weighting parameters to balance the data misfit with the model roughness and result in a single solution which do not provide means to assess the non-uniqueness associated with the inversion. Here, we propose a robust stochastic global search method that considers the objective as a two-component vector and simultaneously minimizes both components: data misfit and model roughness. By providing an estimate of the entire set of the Pareto-optimal solutions, the method allows a better assessment of non-uniqueness than deterministic methods. Since the computational expense of the method increases as the number of objectives and model parameters increase, we parallelized our algorithm to speed up the forward modelling calculations. Applying our inversion to noisy synthetic data sets generated from horizontally stratified earth models for both isotropic and anisotropic assumptions and for different measurement configurations, we demonstrate the accuracy of our method. By comparing the results of our inversion with the regularized genetic algorithm, we also demonstrate the necessity of casting this problem as a multi-objective optimization for a better assessment of uncertainty as compared to a scalar objective optimization method.

scholarly journals Multi-objective optimized breeding strategies

2017 ◽  
Author(s):  
Deniz Akdemir ◽  
Julio Isidro Sánchez
Keyword(s):  
Mathematical Optimization ◽  
Breeding Population ◽  
Data Sets ◽  
Breeding Strategies ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Breeding Programs ◽  
Multi Objective ◽  
Breeding Schemes ◽  
Definition Of

Multi-objective optimization is an emerging field in mathematical optimization which involves optimization a set of objective functions simultaneously. The purpose of most plant and animal breeding programs is to make decisions that will lead to sustainable genetic gains in more than one traits while controlling the amount of co-ancestry in the breeding population. The decisions at each cycle in a breeding program involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by using the multi-objective optimization principles in breeding. The discussion here includes the definition of several multi-objective optimized breeding approaches and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, culling and index selection. We have illustrated the newly proposed methods with two empirical data sets and with simulations.

scholarly journals Fuzzy Clustering Optimal k Selection Method Based on Multi-objective Optimization

2021 ◽  
Author(s):  
Wang Lisong ◽  
Cui Guonan ◽  
Cai Xinye
Keyword(s):  
Fuzzy Clustering ◽  
Optimization Algorithm ◽  
Selection Method ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Number Of Clusters ◽  
K Value ◽  
Calculation Time ◽  
Multi Objective ◽  
Effectiveness Index

Abstract Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, and the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k ) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM's clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multi-objective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value.

Development of Fractional Genetic PSO Algorithm for Multi Objective Data Clustering

2016 ◽  
Vol 7 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Aparna K. ◽  
Mydhili K. Nair
Keyword(s):  
Data Clustering ◽  
Fitness Function ◽  
Pso Algorithm ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Multi Objective ◽  
Stability Measure ◽  
Benchmark Data ◽  
Hybrid Optimization Algorithm

Clustering is the task of finding natural partitioning within a data set such that data items within the same group are more similar than those within different groups. The performance of the traditional K-Means and Bisecting K-Means algorithm degrades as the dimensionality of the data increases. In order to find better clustering results, it is important to enhance the traditional algorithms by incorporating various constraints. Hence it is planned to develop a Multi-Objective Optimization (MOO) technique by including different objectives, like MSE, Stability measure, DB index, XB-index and sym-index. These five objectives will be used as fitness function for the proposed Fractional Genetic PSO algorithm (FGPSO) which is the hybrid optimization algorithm to do the clustering process. The performance of the proposed multi objective FGPSO algorithm will be evaluated based on clustering accuracy. Finally, the applicability of the proposed algorithm will be checked for some benchmark data sets available in the UCI machine learning repository.

scholarly journals Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques

2018 ◽  
Vol 7 (3) ◽  
pp. 68-71
Author(s):  
M. Anusha
Keyword(s):  
Optimization Problems ◽  
Quality Data ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
High Quality ◽  
High Quality Data ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Evolutionary Clustering ◽  
Data Clusters

Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. This paper uses criterion knowledge ranking algorithm solving multi-objective optimization problems. The aim of this research paper is to solve a multi-objective optimization algorithm with close reference point learning method to identify high quality data clusters. A Simple crossover measure is used to quantify the diversity of the whole set, by considering all patterns as a complete entity. In this paper, the task of identifying high quality data clusters using close reference points is proposed to solve multi-objective optimization problem using evolutionary clustering techniques. The proposed algorithm finds the closest feature from the selected features of the data sets that also minimizes the cost while maintains the quality of the solution by producing better convergence. The resultant clusters were analysed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective
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