Non-Dominated Sorting Genetic Quantum Algorithm for Multi-Objective Optimization

2007 ◽  
Author(s):  
Amir-R. Khorsand ◽  
G. Gary Wang ◽  
J. Raghavan
Keyword(s):  
Genetic Algorithm ◽  
Quantum Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
General Interest ◽  
Test Results ◽  
Multi Objective Optimization ◽  
Systematic Comparison ◽  
Multi Objective ◽  
Single Objective

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

A Multi-Objective Optimization Method for Coordinated Control

2011 ◽  
Vol 97-98 ◽  
pp. 942-946
Author(s):  
Yun Feng Gao ◽  
Hua Hu ◽  
Tao Wang ◽  
Xiao Guang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Transmission Model ◽  
Traffic Signal Control ◽  
Cell Transmission Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Combination ◽  
Single Objective

In this paper, to overcome the limitations of the weighted combination and single objective optimization methods, we presented a multi-objective optimization and simulation methodology for network-wide traffic signal control. A multi-objective genetic algorithm based on Non-dominated Sorting Genetic Algorithm II was given to solve the model directly to obtain Pareto optimal solution set. The objectives were evaluated by Enhanced Cell Transmission Model used to describe traffic dynamics on signalized urban road network. The results showed that the single objective optimization method made some of the objectives worsen when the objective to be optimized reaching optimal, and that the weighted combination optimization method gained a compromised solution, but the multi-objective optimization method gave consideration to more objectives, making the number of optimal or suboptimal ones is more than that of worse ones.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

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 Product Configuration Optimization Method Based on Non-Dominated Sorting Genetic Algorithm

2011 ◽  
Vol 215 ◽  
pp. 366-372
Author(s):  
W.H. Sun ◽  
W.C. Lu ◽  
D.Y. Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Product Configuration ◽  
Configuration Design ◽  
Hierarchical Analysis ◽  
Multi Objective Optimization ◽  
Configuration Optimization ◽  
Complex Product ◽  
Multi Objective

In order to realize the complex product rapid configuration design in the environment of mass configuration (MC), the non-dominated sorting genetic algorithm (NGSA) for product rapid configuration design is proposed in this paper. The model of multi-objective product configuration optimization is established, and hierarchical analysis is made for configuration design. By comparing the similarity and integrity of requirement and instance, the sequence of retrieval instances is given according to the reuse degree, and multi-objective optimization configuration based on NGSA is realized. Finally, the validity and practicability of the method is verified by an instance which is applied in rapid configuration design of the drive module of tuyere puncher.

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.

Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

Robotica ◽  
2018 ◽  
Vol 36 (6) ◽  
pp. 839-864 ◽  
Author(s):  
Abdur Rosyid ◽  
Bashar El-Khasawneh ◽  
Anas Alazzam
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Weighted Sum ◽  
Parallel Kinematics ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Cartesian Stiffness Matrix ◽  
Optimal Value ◽  
Single Objective

SUMMARYThis paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

Investigation on the Multi-Objective Optimization of Supercritical Airfoil Based on Nondominated Sorting Genetic Algorithm

2013 ◽  
Vol 444-445 ◽  
pp. 357-362 ◽  
Author(s):  
Da Wei Liu ◽  
Xin Peng ◽  
Xin Xu ◽  
De Hua Chen
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Method ◽  
Parametric Modeling ◽  
Multi Objective Optimization ◽  
Supercritical Airfoil ◽  
Multi Objective ◽  
Time Optimization ◽  
Genetic Algorithm Method ◽  
Nondominated Sorting

This paper aimed to investigate the multi-objective optimization method of supercritical airfoil. To achieve the optimal design of supercritical airfoil Rae2822, an improved NSGA-2 (Nondominated Sorting Genetic Algorithm) method was utilized, while the cross-operator and adaptive-variation operator were introduced to improve the convergence speed of the algorithm. During the optimization, the airfoil parametric modeling was achieved based on the Bezier-Bernstein method, and the objective function was obtained through solving the N-S equations. Considering the parallel computation characteristics of the algorithm, the computation was conducted in large-scale Linux computer system to reduce the solving time. Optimization results showed that the undominate solution with high quality obtained through the NSGA-2 method distributed evenly, which provided the designer a wider choosing space. It was also showed that the multi-objective optimization method presented in this paper was feasible and reliable.

scholarly journals Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

1999 ◽  
Vol 7 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

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