BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems

2013 ◽  
Vol 40 (1) ◽  
pp. 282-302 ◽  
Author(s):  
Xiang Li ◽  
Gang Du
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

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

A Modified Non-Dominated Sorting Genetic Algorithm with Fractional Factorial Design for Multi-Objective Optimization Problems

2010 ◽  
Vol 26 (2) ◽  
pp. 143-156
Author(s):  
J.-L. Liu ◽  
T.-F. Lee
Keyword(s):  
Genetic Algorithm ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Optimization Problems ◽  
Fractional Factorial ◽  
Good Genes ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mutation Operators ◽  
Engineering Design Problems

AbstractThis study develops an intelligent non-dominated sorting genetic algorithm (GA), called INSGA herein, which includes a non-dominated sorting, crowded distance sorting, binary tournament selection, intelligent crossover and non-uniform mutation operators, for solving multi-objective optimization problems (MOOPs). This work adopts Goldberg's notion of non-dominated sorting and Deb's crowded distance sorting in the proposed MOGA to achieve solutions with good diversity-preservation and uniform spread on the approximated Pareto front. In addition, the chromosomes of offspring are generated based on an intelligent crossover operator using a fractional factorial design to select good genes from parents intelligently and achieve the goals of fast convergence and high numerical accuracy. To further improve the fine turning capabilities of the presented MOGA, a non-uniform mutation operator is also applied. A typical mutation approach is to create a random number and then add it to corresponding original value. Performance evaluation of the INSGA is examined by applying it to a variety of unconstrained and constrained multi-objective optimization functions. Moreover, two engineering design problems, which include a two-bar truss design and a welded beam design, are studied by the proposed INSGA. Results include the estimated Pareto-optimal front of non-dominated solutions.

scholarly journals Genetic Algorithm for the Multi-Objective Optimization of Product Gene

2012 ◽  
Vol 6-7 ◽  
pp. 116-121
Author(s):  
Qing Song Ai ◽  
Zhou Liu ◽  
Yan Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Engineering ◽  
Manufacturing Industry ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimal Solution ◽  
Adaptive Mutation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

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