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>

scholarly journals Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

2008 ◽  
Vol 16 (3) ◽  
pp. 385-416 ◽  
Author(s):  
Shengxiang Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Population Diversity ◽  
Dynamic Problems ◽  
Dynamic Optimization Problems ◽  
Hybrid Memory ◽  
Memory Schemes ◽  
Memory Scheme ◽  
With Memory

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1806
Author(s):  
Yuedong Zhang ◽  
Yuanbin Mo
Keyword(s):  
Dynamic Optimization ◽  
Production Efficiency ◽  
Chemical Engineering ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Population Diversity ◽  
Equal Division ◽  
Dynamic Optimization Problems ◽  
Increase Production Efficiency ◽  
Chaotic Mapping

The optimal solution of the chemical dynamic optimization problem is the basis of automatic control operation in the chemical process, which can reduce energy consumption, increase production efficiency, and maximize economic benefit. In this paper, a modified sailfish optimizer (MSFO) combined with an equal division method is proposed for solving chemical dynamic optimization problems. Based on the basic sailfish optimizer, firstly, the tent chaotic mapping strategy is introduced to disturb the initialization of sailfish and sardine populations to avoid the loss of population diversity. Secondly, an adaptive linear reduction strategy of attack parameters is proposed to enhance the exploration and exploitation ability of sailfish. Thirdly, the updating formula of sardine position is modified, and the global optimal solution is used to attract all sardine positions, which can avoid the premature phenomenon of the algorithm. Eventually, the MSFO is applied to solve six classical optimization cases of chemical engineering to evaluate its feasibility. The experimental results are analyzed and compared with other optimization methods to prove the superiority of the MSFO in solving chemical dynamic optimization problems.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

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.

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