A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm
作者：
Highlights：
• A hybrid optimization approach integrating both BPNN and GA is proposed.
• Standard Levenberg–Marquardt training algorithm is modified to accelerate the BPNN convergence.
• Simulated annealing algorithm is embedded into GA to enhance its local searching ability.
• Effectiveness of the proposed approach is demonstrated via its application in an engineering field.
• Results show that desired thickness in blow molded parts can be obtained via only fewer experimental trials.
摘要
•A hybrid optimization approach integrating both BPNN and GA is proposed.•Standard Levenberg–Marquardt training algorithm is modified to accelerate the BPNN convergence.•Simulated annealing algorithm is embedded into GA to enhance its local searching ability.•Effectiveness of the proposed approach is demonstrated via its application in an engineering field.•Results show that desired thickness in blow molded parts can be obtained via only fewer experimental trials.
论文关键词：Iteration optimization,Backpropagation neural network,Genetic algorithm,Blow molding
论文评审过程：Available online 4 August 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.07.039
