Genetic Algorithms-Based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries

2017 ◽  
pp. 151-166
Author(s):  
AbdelMonaem F. M. AbdAllah ◽  
Daryl L. Essam ◽  
Ruhul A. Sarker
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems

2017 ◽  
Vol 6 (3) ◽  
pp. 139
Author(s):  
Al-khafaji Amen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Uncertain Environments ◽  
Convergence Problem ◽  
Royal Road

<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.</span>

Genetic Algorithms with Immigrants Scheme for Dynamic Optimization Problems

2013 ◽  
Vol 333-335 ◽  
pp. 1379-1383
Author(s):  
Yan Wu ◽  
Xiao Xiong Liu
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Main Idea ◽  
Optimal Solution ◽  
Search Space ◽  
Dynamic Environments ◽  
Dynamic Optimization Problems ◽  
New Space ◽  
Over Time

In dynamic environments, it is difficult to track a changing optimal solution over time. Over the years, many approaches have been proposed to solve the problem with genetic algorithms. In this paper a new space-based immigrant scheme for genetic algorithms is proposed to solve dynamic optimization problems. In this scheme, the search space is divided into two subspaces using the elite of the previous generation and the range of variables. Then the immigrants are generated from both the subspaces and inserted into current population. The main idea of the approach is to increase the diversity more evenly and dispersed. Finally an experimental study on dynamic sphere function was carried out to compare the performance of several genetic algorithms. The experimental results show that the proposed algorithm is effective for the function with moving optimum and can adapt the dynamic environments rapidly.

An algorithm comparison for dynamic optimization problems

2012 ◽  
Vol 12 (10) ◽  
pp. 3176-3192 ◽  
Author(s):  
Ignacio G. del Amo ◽  
David A. Pelta ◽  
Juan R. González ◽  
Antonio D. Masegosa
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Algorithm Comparison
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