scholarly journals An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangmin Guan ◽  
Xuejun Zhang ◽  
Yanbo Zhu ◽  
Dengfeng Sun ◽  
Jiaxing Lei
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Network Flow ◽  
Optimization Problems ◽  
Parallel Evolution ◽  
Dynamic Adjustment ◽  
Cooperative Coevolution ◽  
Solution Quality ◽  
Optimization Framework ◽  
Flow Assignment

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

scholarly journals A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Long ◽  
Changzhi Wu ◽  
Xiangyu Wang ◽  
Lin Jiang ◽  
Jueyou Li
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Selection Procedure ◽  
Population Based ◽  
Pareto Solution ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiobjective Optimization Problems ◽  
Optimum Order

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

scholarly journals Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Hong Zhang ◽  
Guangchen Bai ◽  
Lukai Song
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimal Algorithm ◽  
Multicomponent System ◽  
Kriging Model ◽  
Surface Model ◽  
Optimization Framework ◽  
Comparison Results ◽  
Multiobjective Design

To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

scholarly journals An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-26
Author(s):  
Jiuyuan Huo ◽  
Liqun Liu
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Resource Planning ◽  
Planning Problem ◽  
Abc Algorithm ◽  
Optimization Framework ◽  
Bee Colony ◽  
Multiobjective Optimization Problems ◽  
Evolution Algorithms

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms.

Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

2000 ◽  
Vol 8 (2) ◽  
pp. 149-172 ◽  
Author(s):  
Joshua D. Knowles ◽  
David W. Corne
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Population Based ◽  
Evolution Strategy ◽  
Basic Algorithm ◽  
Pareto Optimal Set ◽  
Candidate Solution ◽  
Multiobjective Optimization Problems

We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1+1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1+1)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1+λ) and (μ+λ) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.

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