A method of genetic algorithm based multiobjective optimization via cooperative coevolution

2006 ◽  
Vol 20 (12) ◽  
pp. 2115-2123 ◽  
Author(s):  
Jongsoo Lee ◽  
Doyoung Kim
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Cooperative Coevolution

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 Multiobjective optimization design of the solar field and reverse osmosis system with preheating feed water using Genetic algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 624-642 ◽  
Author(s):  
Iman Ebrahimi Ghoujdi ◽  
Hasti Hadiannasab ◽  
Mokhtar Bidi ◽  
Abbas Naeimi ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Reverse Osmosis ◽  
Optimization Design ◽  
Feed Water ◽  
Solar Field

scholarly journals An Evolutionary Multiagent Framework for Multiobjective Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Zihui Zhang ◽  
Qiaomei Han ◽  
Yanqiang Li ◽  
Yong Wang ◽  
Yanjun Shi
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Layout Optimization ◽  
Layout Problem ◽  
System Decomposition ◽  
System Memory ◽  
Satellite Equipment ◽  
Complete Solutions ◽  
Evolutionary Mode

This article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO-EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem.

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.

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