scholarly journals A Novel Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: DSGA

2021 ◽  
Author(s):  
Qiang Long ◽  
Guoquan Li ◽  
Lin Jiang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Large Scale ◽  
Computational Cost ◽  
Critical Component ◽  
Multi Objective Optimization ◽  
Sorting Method ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
General Scale

Abstract Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (DNS). It is based on the sorting of each objective instead of dominance comparisons. The computational compelxity of DNS is $O(mN\log N)$ ($m$ is the number of objectives, $N$ is the population size), which equals to the best record so far. Based on DNS, we introduce a novel multi-objective genetic algorithm (MOGA) called the dynamic non-dominated sorting genetic algorithm (DSGA). Then, some numerical comparisons between different non-dominated sorting method are presented. The results shows that DNS is efficient and promising. Finally, numerical experiments on DSGA are also given. The results show that DSGA outperforms some other MOEAs both on general-scale and large-scale multi-objective problems.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 314-317
Author(s):  
Di Wu ◽  
Sheng Yao Yang ◽  
J.C. Liu
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Performance Optimization ◽  
Optimization Problem ◽  
Simulation Analysis ◽  
Multi Objective Optimization ◽  
Weighting Method ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Decision Engine

The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.

A New Interval Multi-Objective Optimization Method for Uncertain Problems with Dependent Interval Variables

2020 ◽  
Vol 17 (10) ◽  
pp. 2050007
Author(s):  
Guiping Liu ◽  
Rui Luo ◽  
Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Iterative Algorithm ◽  
Correlation Coefficients ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Interval Model ◽  
Numerical Examples ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Interval Variables

In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

A Multi-Objective Optimization Genetic Algorithm for SOPC Hardware-Software Partitioning

2012 ◽  
Vol 457-458 ◽  
pp. 1142-1148
Author(s):  
Fu Yang ◽  
Liu Xin ◽  
Pei Yuan Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Multi Objective Optimization ◽  
Key Technology ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Partitioning Problem ◽  
System On Programmable Chip ◽  
Software Partitioning

Hardware-software partitioning is the key technology in hardware-software co-design; the results will determine the design of system directly. Genetic algorithm is a classical search algorithm for solving such combinatorial optimization problem. A Multi-objective genetic algorithm for hardware-software partitioning is presented in this paper. This method can give consideration to both system performance and indicators such as time, power, area and cost, and achieve multi-objective optimization in system on programmable chip (SOPC). Simulation results show that the method can solve the SOPC hardware-software partitioning problem effectively.

Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm

2018 ◽  
Vol 45 (11) ◽  
pp. 973-985
Author(s):  
Yuan-Yang Zou ◽  
Xue-Guo Xu ◽  
Gui-Hua Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Efficient Algorithm ◽  
Numerical Experiments ◽  
Adaptive System ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Objective Optimization ◽  
New Model ◽  
Multi Objective

In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.

A Hybrid UKF-MAG Algorithm for Finite Element Model Updating of Historical Constructions

2019 ◽  
Author(s):  
Javier Naranjo-Pérez ◽  
Andrés Sáez ◽  
Javier F. Jiménez-Alonso ◽  
Pablo Pachón ◽  
Víctor Compán
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Kalman Filter ◽  
Finite Element Model ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Element Model ◽  
Fe Model ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

<p>The finite element model (FE) updating is a calibration method that allows minimizing the discrepancies between the numerical and experimental modal parameters. As result, a more accurate FE model is obtained and the structural analysis can represent the real behaviour of the structure. However, it is a high computational cost process. To overcome this issue, alternative techniques have been developed. This study focuses on the use of the unscented Kalman filter (UKF), which is a local optimization algorithm based on statistical estimation of parameters taken into account the measurements. The dome of a real chapel is considered as benchmark structure. A FE model is updated applying two different algorithms: (i) the multi-objective genetic algorithm and (ii) a hybrid unscented Kalman filter-multi-objective genetic algorithm (UKF-MGA). Finally, a discussion of the results will be presented to compare the performance of both algorithms.</p>

Some Preliminary Results on the Development and Comparison of a New Multi-Objective Genetic Algorithm

2001 ◽  
Author(s):  
A. Farhang-Mehr ◽  
J. Wu ◽  
S. Azarm
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Quality Metrics ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Preliminary Results ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Speed Reducer

Abstract Some preliminary results for a new multi-objective genetic algorithm (MOGA) are presented. This new algorithm aims at obtaining the fullest possible representation of observed Pareto solutions to a multi-objective optimization problem. The algorithm, hereafter called entropy-based MOGA (or E-MOGA), is based on an application of the concepts from the statistical theory of gases to a MOGA. A few set quality metrics are introduced and used for a comparison of the E-MOGA to a previously published MOGA. Due to the stochastic nature of the MOGA, confidence intervals with a 95% confidence level are calculated for the quality metrics based on the randomness in the initial population. An engineering example, namely the design of a speed reducer is used to demonstrate the performance of E-MOGA when compared to the previous MOGA.

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