Nonlinear Function Optimization Based on Adaptive Genetic Algorithm

2020 ◽  
Author(s):  
Lihua Lei ◽  
Naijin Liu ◽  
Ju Zhou
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Function ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Nonlinear Function Optimization

Approach for Solving Nonlinear Equation Group Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1636-1639
Author(s):  
Hong Lian Shen ◽  
Feng Lin Cheng ◽  
Huan Ru Ren
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Accurate Solution ◽  
Initial Solution ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Time Efficiency ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Equation Group

A numeric method of solving nonlinear equation group is proposed. The problem of solving nonlinear equation group is equivalently changed to the problem of function optimization, and then a solution is obtained by adaptive genetic algorithm, considering it as the initial solution of Levenberg-Marquardt algorithm, a more accurate solution can be obtained, as a result, time efficiency is improved.

MEMS Relay Optimization Design Algorithm Based on Particle Swarm Optimization

2013 ◽  
Vol 562-565 ◽  
pp. 155-161
Author(s):  
Han Min Liu ◽  
Qing Hua Wu ◽  
Xue Song Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Design Algorithm ◽  
Nonlinear Function Optimization

Mathematical model of the MEMS relay volume involves in mechanical, electrical, magnetic, thermal, etc., the MEMS relay optimization design is a constrained nonlinear function optimization problem. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to other heuristic methods and is a powerful optimization algorithm for MEMS relay optimization design.

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.

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