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 A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

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.

Analyzing the Objective Functions for Multi-Objective Optimization of the Amplifier Adaptive Control of Operating Point

2021 ◽  
Author(s):  
Erick A. Barboza ◽  
Carmelo J. A. Bastos-Filho ◽  
Daniel A. R. Chaves ◽  
Joaquim F. Martins-Filho ◽  
Leonardo D. Coelho ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Operating Point ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective

ROBUSTNESS OF THE MULTI‐OBJECTIVE MOORA METHOD WITH A TEST FOR THE FACILITIES SECTOR

2009 ◽  
Vol 15 (2) ◽  
pp. 325-375 ◽  
Author(s):  
Willem K. Brauers ◽  
Edmundas K. Zavadskas
Keyword(s):  
Linear Equation ◽  
Reference Point ◽  
Error Term ◽  
Cost Price ◽  
Common Language ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Definition Of ◽  
Moora Method

The definition of robustness in econometrics, the error term in a linear equation, was not only broadened, but in addition moved to the meaning of common language: from a cardinal to a qualitative one. These interpretations were tested by an application on the Facilities Sector in Lithuania. The application is multi‐objective: like costs, experience and effectiveness at the side of the contractors; quality, duration of the work and cost price at the side of the owners. These objectives having all different units the dimensionless ratios of the MOORA method avoids the difficulties of normalization. In a first part of MOORA these ratios were aggregated and in a second one they were used as distances to a reference point. The results of both parts control each other, a test on robustness. Additionally, MOORA shows a robust domination on all other methods of multi‐objective optimization. For the Facilities Sector in Lithuania, both parts of MOORA resulted in a comparable ranking. In this way a double check was made on the robustness of the results. Santrauka Patikimumo apibrėžimas ekonometrikoje, kaip neteisingas terminas tiesinėje lygtyje, buvo ne tik papildytas, bet ir išreikštas įprasta kalba: nuo kiekybinio prie kokybinio. Šios interpretacijos buvo patikrintos taikant jas Lietuvos paslaugų sektoriuje. Taikymas yra daugiatikslis: iš rangovo pusės kaip išlaidos, patirtis, efektyvumas; kokybė, darbo trukmė, kaina iš užsakovo pusės. Minėtieji tikslai turi skirtingus matavimo vienetus. O jų santykiniai dydžiai neturi mato vienetų, todėl taikant MOORA metodą yra išvengiama sunkumų juos normalizuojant. Pirmoje MOORA metodo taikymo dalyje šie santykiai yra sujungiami, o antroje dalyje ieškoma atstumo iki geriausio sprendinio. Abiejų metodo dalių rezultatai pagrindžia sprendinio teisingumą. Tai rodo aiškų MOORA metodo pranašumą, palyginti su kitais daugiatikslio optimizavimo metodais. Taikant abi MOORA metodo dalis Lietuvos paslaugų sektoriui buvo sudarytas lyginamasis rangavimas, buvo atliktas dvigubas rezultatų patikimumo patikrinimas.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

scholarly journals Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1845 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Rujiang Hao ◽  
Zechao Liu
Keyword(s):  
Frequency Band ◽  
Background Noise ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Rolling Element Bearings ◽  
Grey Wolf ◽  
Objective Functions ◽  
Multi Objective ◽  
Rolling Element ◽  
Envelope Spectrum

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

2009 ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Interval Uncertainty ◽  
Minimal Amount ◽  
Engineering System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

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.

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