An Improved Fuzzy Adaptive Genetic Algorithm for Function Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 2598-2601
Author(s):  
Lan Yao ◽  
Yu Lian Jiang ◽  
Jian Xiao
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Adaptive Selection ◽  
Mutation Probability ◽  
Individual Fitness ◽  
Fitness Value ◽  
Crossover Probability ◽  
Fuzzy Adaptive

The critical operators for genetic algorithms to avoid premature and improve globe convergence is the adaptive selection of crossover probability and mutation probability. This paper proposed an improved fuzzy adaptive genetic algorithm in which the variance of population and individual fitness value are used to measure the overall population diversity and individual difference, meanwhile, both of them are applied to design fuzzy reference system for adaptively estimation of crossover probability and mutation probability. Simulation results of function optimization show that the new algorithm can converge faster and is more effective at avoiding premature convergence in comparison with standard genetic algorithm.

Optimal Configuration of PMU in Power System

2015 ◽  
Vol 734 ◽  
pp. 659-665
Author(s):  
Jing Zhi ◽  
Yan Xu
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Phasor Measurement Unit ◽  
Measurement Unit ◽  
Adaptive Genetic Algorithm ◽  
Mutation Probability ◽  
Fitness Value ◽  
Crossover Probability ◽  
Minimum Number ◽  
Average Fitness

In order to use the least number of phasor measurement unit (PMU) to guarantee the power system completely observable, an optimal PMU configuration method in power system was put forward. The pre-configuration of PMU was done considering the actual situation of power grid. The genetic algorithm (GA) was used for PMU configuration. Modify the formulae of crossover probability and mutation probability in traditional genetic algorithm to overcome the evolutionary stagnation when the maximum fitness value and the average fitness value in group were equal. The improved adaptive genetic algorithm (IAGA) was obtained. In order to eliminate the premature convergence of GA resulted from the chance and randomness of the crossover operation and mutation operation, the preventing premature operation was introduced. This method combined the IAGA and the preventing premature operation. It has good global astringency, and it can ensure the network complete observability with the minimum number of PMU.

scholarly journals Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi

2014 ◽  
Vol 10 (1) ◽  
pp. 189
Author(s):  
Zulfahmi Indra ◽  
Subanar Subanar
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Migration Rate ◽  
Adaptive Genetic Algorithm ◽  
Chain Management ◽  
Stepping Stone ◽  
Mutation Probability ◽  
Crossover Probability ◽  
Distributed Genetic Algorithm

AbstrakManajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825. Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi. Abstract Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825. Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.

scholarly journals A Parameter Adaptive Genetic Algorithm Based Service Compositions

2018 ◽  
Vol 173 ◽  
pp. 03051
Author(s):  
Huizhou Yang ◽  
Li Zhang
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Mutation Probability ◽  
Adaptive Parameter ◽  
Main Challenge ◽  
Composition Problem ◽  
Crossover Probability ◽  
Parameter Adaptive

How to select and combine many services with similar functions reasonably and efficiently to provide users with better service is the main challenge in the service composition problem. This is thorny when the number of the candidate Services is huge. Recently, researches transform the service compositions problem as a multi-objective optimizing task, and then the genetic algorithm is commonly used to tackle this issue. However, the fixed crossover probability and mutation probability settings in genetic algorithm usually result to it falls into a local optimal. To improve the performance of the genetic algorithm in the service composition task, this paper proposes an adaptive parameter adjust strategy, which can adjust the crossover probability and mutation probability automatically. The experiment result shows our method has greatly improved the maximum fitness of the final solutions of traditional genetic algorithm.

Application of Control Parameters Optimization of CNC Servo System Based on Self-Adaptive Genetic Algorithm

2011 ◽  
Vol 239-242 ◽  
pp. 2847-2850
Author(s):  
Gui Rong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Population Diversity ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Fitness Value ◽  
Current Engineering ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Self Adaptive

In order to solve the shortcomings of current engineering methods for parameters adjustment that can only deal with them according to single requirement of system and can not optimize them in the whole range, as well as the standard genetic algorithm is prone to premature convergence, therefore, an improved PID parameters adjustment method based on self-adaptive genetic algorithm was proposed. This approach enables crossover and mutation probability automatically change along with the fitness value, not only can it maintain the population diversity, but also can ensure the convergence of the algorithm. A comparison of the dynamic response between the traditional PID control and the PID control based on self-adaptive genetic algorithm was made. Simulation results show that the latter has much superiority.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

Sign in / Sign up

Export Citation Format

Share Document