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.

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.

Edge Tracing Based on Improved Genetic Algorithm

2012 ◽  
Vol 488-489 ◽  
pp. 904-912
Author(s):  
Bin Bing Liu ◽  
Hai Qing Chen ◽  
Chong Huang ◽  
Zhen Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Polar Coordinates ◽  
Improved Genetic Algorithm ◽  
Chain Code ◽  
Combinatorial Optimization Problems ◽  
Gene Coding ◽  
Robustness To Noise ◽  
Edge Tracing ◽  
Tracing Method

In this paper, we proposed a new edge tracing method with high robustness to noise. Through representing edge with maximal gradient path encoded by chain code, the edge tracing problems can be converted into combinatorial optimization problems, and so they can be solved by genetic algorithm. We optimized the traditional genetic algorithm in order to improve the convergence rate. Our method is effective to edges with any shape because it does not require any prior knowledge about the edges. In this paper we also discussed the problem of edge winding and folding and expatiated how to avoid it by designing proper gene coding method and punishment function. Furthermore, by transforming the region of interests from Cartesian coordinates to polar coordinates before edge tracing, this method can be used for closed edges. The experimental results show this is an effective edge tracing method with high robustness and flexibility.

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 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.

scholarly journals Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1092
Author(s):  
Qing Duan ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Xingping Sun ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Sequence ◽  
Engineering Optimization ◽  
Exploration And Exploitation ◽  
Salp Swarm Algorithm ◽  
Symmetric Perturbation ◽  
Swarm Algorithm ◽  
Search Capability ◽  
Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

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 New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window

2017 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Lahcene Guezouli ◽  
Samir Abdelhamid
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Vehicle Routing Problem ◽  
Processing Time ◽  
Optimization Problems ◽  
Time Window ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Capacitated Vehicle

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

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