Hybrid Genetic Algorithm and its Application

2011 ◽  
Vol 183-185 ◽  
pp. 1090-1093
Author(s):  
Hai Tao Xin
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Global Search ◽  
Function Optimization ◽  
Gradient Algorithm

A new hybrid algorithm that incorporates the gradient algorithm into the orthogonal genetic algorithm is presented in this paper. The experiments showed that it can achieve better performance by performing global search and local search alternately. The new algorithm can be applied to solve the function optimization problems efficiently.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

scholarly journals Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiquan Wang ◽  
Zhiwen Cheng ◽  
Okan K. Ersoy ◽  
Panli Zhang ◽  
Weiting Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Global Search ◽  
Single Mutation ◽  
Constrained Optimization Problems ◽  
Fitness Value ◽  
Steering Mechanism ◽  
Real Coded Genetic Algorithm

An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization problems. First, a sorting grouping selection method is given with the advantage of easy realization and not needing to calculate the fitness value. Secondly, a heuristic normal distribution crossover (HNDX) operator is proposed. It can guarantee the cross-generated offsprings to locate closer to the better one among the two parents and the crossover direction to be very close to the optimal crossover direction or to be consistent with the optimal crossover direction. In this way, HNDX can ensure that there is a great chance of generating better offsprings. Thirdly, since the GA in the existing literature has many iterations, the same individuals are likely to appear in the population, thereby making the diversity of the population worse. In IRCGA, substitution operation is added after the crossover operation so that the population does not have the same individuals, and the diversity of the population is rich, thereby helping avoid premature convergence. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, this paper proposes a combinational mutation method, which makes the mutation operation take into account both local search and global search. The computational results with nine examples show that the IRCGA has fast convergence speed. As an example application, the optimization model of the steering mechanism of vehicles is formulated and the IRCGA is used to optimize the parameters of the steering trapezoidal mechanism of three vehicle types, with better results than the other methods used.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2021 ◽  
pp. 148-180
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

The Truss Structural Optimization Design Based on Improved Hybrid Genetic Algorithm

2010 ◽  
Vol 163-167 ◽  
pp. 2304-2308
Author(s):  
Feng Guo Jiang ◽  
Zhen Qing Wang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Structural Optimization ◽  
Optimization Design ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
General Search ◽  
Genetic Arithmetic ◽  
Iteration Analysis

Genetic arithmetic operators in genetic algorithm be improved , and a hybrid genetic algorithm of a gradient algorithm combining with the genetic algorithm be given against to the defects such as premature，slow on convergence rate，weak in the ability of local search ，all these appeared on the progress of genetic algorithm's iteration. Analysis result indicate that not only strong on the local search capacity of gradient algorithm be exhibited but also strong on the general search capacity of genetic algorithm be combined based on the hybrid genetic algorithm ,which make phenomenon of premature avoid, and the rate of convergence be improved greatly. Concrete calculated example indicated that the hybrid genetic algorithm is an effective structural optimization method.

Genetic Algorithm Based on Chaos Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 641-645
Author(s):  
Chun Bo Xiu ◽  
Li Fen Lu ◽  
Yi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Global Solutions ◽  
Function Optimization ◽  
Chaos Optimization ◽  
Fast Convergence Rate ◽  
Simulation Results ◽  
Searching Ability ◽  
Two Stages

A hybrid genetic algorithm is proposed based on chaos optimization. The optimization process can be divided into two stages every iteration, one is genetic coarse searching and the other is chaos elaborate searching. Genetic algorithm searches the global solutions in the origin space. An elaborate space near the center of superior individuals is divided from the origin space, which is searched by chaos optimization adequately to generate new better superior individuals for genetic operation. The elaborate space can be compressed quickly to accelerate searching rate and enhance the searching efficiency. In this way, the algorithm has global searching ability and fast convergence rate. The simulation results prove that the algorithm can give satisfied results to function optimization problems.

Improved Genetic Algorithm Based on the Simulated Annealing and Its Application

2016 ◽  
Vol 13 (10) ◽  
pp. 6495-6500
Author(s):  
He-Xuan Hu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Local Search ◽  
Optimization Problems ◽  
Global Search ◽  
Letter Recognition ◽  
Improved Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Low Efficiency ◽  
Search Capability

Genetic Algorithm (GA) is an adaptive algorithm of global search optimization formed through the simulation of biological heredity and evolution in the natural environment. By the random selection, the algorithm requires no special needs for the search space and derivations, which is featured with simple operation, rapid convergence, and other advantages. Therefore, it is especially applicable for complex and non-linear problems that are difficult to be solved by the conventional search methods. However, this algorithm is strong in global search capability but insufficient in the local search capability. Simulated annealing (SA) is an algorithm possessed with the stronger local search ability and widely used in combinatorial optimization problems. Due to the inadequate local search capability of GA and deficient global search capability of SA, they were combined in the paper to complement their mutual advantages and take use of the global search capability of GA and local search capability of SA. The poor local search ability of GA and its premature convergence as well as the bad global search capability of SA and its low efficiency were overcome, and the SA-based mixed GA was constructed. Then, standard data sets of wine and letter-recognition in the UCI database were applied for the verification of the algorithm. It was indicated that the convergence rate was improved to some extent by the mixed algorithm proposed in this paper. Finally, the improved genetic algorithm was applied to the actual projects, which indicated the feasibility of the algorithm in engineering.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2017 ◽  
pp. 230-268 ◽  
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

scholarly journals An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jiquan Wang ◽  
Mingxin Zhang ◽  
Okan K. Ersoy ◽  
Kexin Sun ◽  
Yusheng Bi
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Normal Distribution ◽  
Optimization Problems ◽  
Global Search ◽  
Single Mutation ◽  
Beam Structure ◽  
Great Chance ◽  
Real Coded Genetic Algorithm ◽  
Cantilevered Beam

A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.

Application of Hybrid Genetic Algorithm in Control Network Adjustment

2013 ◽  
Vol 353-356 ◽  
pp. 3434-3437
Author(s):  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Local Search ◽  
Simplex Method ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Experimental Results ◽  
Control Network ◽  
Network Adjustment ◽  
Search Speed

In this paper a hybrid genetic algorithm which consists of the simplex method and the genetic algorithm is proposed for the defect of poor local search ability of genetic algorithm. The hybrid genetic algorithm has the advantages of good global convergence of the genetic algorithm and excellent local search ablility of the simplex method and can improve search speed and calculation accuracy.The hybrid algorithm is applied to the control network adjustment and experimental results demonstrates the effectiveness and superiority of the algorithm.

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