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

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.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huaixiao Wang ◽  
Jianyong Liu ◽  
Jun Zhi ◽  
Chengqun Fu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Function Optimization ◽  
The Self ◽  
Improved Method ◽  
Simulation Test ◽  
Quantum Genetic Algorithm ◽  
Mutation Operation

To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

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.

scholarly journals Leaders and followers algorithm for constrained non-linear optimization

2019 ◽  
Vol 13 (1) ◽  
pp. 162
Author(s):  
Helen Yuliana Angmalisang ◽  
Syaiful Anam ◽  
Sobri Abusini
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Linear Constraints ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Leaders And Followers ◽  
Non Linear ◽  
Dimensional Optimization

<p>Leaders and Followers algorithm was a novel metaheuristics proposed by Yasser Gonzalez-Fernandez and Stephen Chen. In solving unconstrained optimization, it performed better exploration than other well-known metaheuristics, e.g. Genetic Algorithm, Particle Swarm Optimization and Differential Evolution. Therefore, it performed well in multi-modal problems. In this paper, Leaders and Followers was modified for constrained non-linear optimization. Several well-known benchmark problems for constrained optimization were used to evaluate the proposed algorithm. The result of the evaluation showed that the proposed algorithm consistently and successfully found the optimal solution of low dimensional constrained optimization problems and high dimensional optimization with high number of linear inequality constraint only. Moreover, the proposed algorithm had difficulty in solving high dimensional optimization problem with non-linear constraints and any problem which has more than one equality constraint. In the comparison with other metaheuristics, Leaders and Followers had better performance in overall benchmark problems.</p>

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 Optimization Of Thermo-Electric Coolers Using Hybrid Genetic Algorithm And Simulated Annealing

2014 ◽  
Vol 24 (2) ◽  
pp. 155-176 ◽  
Author(s):  
Doan V.K. Khanh ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthi ◽  
Vo N. Dieu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Coefficient Of Performance ◽  
Design Parameters ◽  
Leg Length ◽  
Thermo Electric ◽  
Material Parameters ◽  
Wide Range

Abstract Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to their superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environmentally friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single-objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) and Simulated Annealing (SA) to optimize geometry properties so that TECs will operate at near optimal conditions. Equality constraint and inequality constraint were taken into consideration.

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.

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