Volume 14, Issue 2 (2-2024)
IJOCE 2024, 14(2): 189-210 |
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Roudak M A, Shayanfar M A, Farahani M, Badiezadeh S, Ardalan R. AN ENHANCED GENETIC ALGORITHM BASED ON THE INTRODUCTION OF FIXED STATION GROUPS AND A NEW VARIABLE MULTI-PARENT CROSSOVER TECHNIQUE. IJOCE 2024; 14 (2) :189-210
URL: http://ijoce.iust.ac.ir/article-1-582-en.html
URL: http://ijoce.iust.ac.ir/article-1-582-en.html
1- Department of Civil Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
2- School of Civil Engineering, Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Tehran, Iran
2- School of Civil Engineering, Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (5617 Views)
Genetic algorithm is a robust meta-heuristic algorithm inspired by the theory of natural selection to solve various optimization problems. This study presents a method with the purpose of promoting the exploration and exploitation of genetic algorithm. Improvement in exploration ability is made by adjusting the initial population and adding a group of fixed stations. This modification increases the diversity among the solution population, which enables the algorithm to escape from local optimum and to converge to the global optimum even in fewer generations. On the other hand, to enhance the exploitation ability, increasing the number of selected parents is suggested and a corresponding crossover technique has been presented. In the proposed technique, the number of parents to generate offspring is variable during the process and it could be potentially more than two. The effectiveness of the modifications in the proposed method has been verified by examining several benchmark functions and engineering design problems.
Keywords: Genetic algorithm, meta-heuristic optimization, fixed station groups, variable multi-parent crossover.
Type of Study: Research |
Subject:
Optimal design
Received: 2024/04/11 | Accepted: 2024/02/21 | Published: 2024/02/21
Received: 2024/04/11 | Accepted: 2024/02/21 | Published: 2024/02/21
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