2006Shuxue de shijian yu renshiRequires access

Hybrid Genetic Algorithm for Topology and Layout Optimization of Oilfield Water Injection System

Yang Liu

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Abstract

An optimization model of topology and layout optimization for water injection system is established,in which the minimum investment is taken as objective function.According to the model characteristics,the problem is divided into two layers,the genetic algorithm and nonlinear optimization are used to solve.The operational process of genetic algorithm is improved,the fitness function is adjusted,the cross and mutation method are improved,simulated annealing algorithm is combined with,and restrictions are satisfied,infeasible solutions are reduced,optimum performance of genetic algorithm is enhanced.Optimization results show that the algorithm is efficient.

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What this paper is about

An optimization model of topology and layout optimization for water injection system is established,in which the minimum investment is taken as objective function.According to the model characteristics,the problem is divided into two layers,the genetic algorithm and nonlinear optimization are used to solve.The operational process of genetic algorithm is improved,the fitness function is adjusted,the cross and mutation method are improved,simulated annealing algorithm is combined with,and restrictions are satisfied,infeasible solutions are reduced,optimum performance of genetic algorithm is enhanced.Optimization results show that the algorithm is efficient.

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Available abstract

An optimization model of topology and layout optimization for water injection system is established,in which the minimum investment is taken as objective function.According to the model characteristics,the problem is divided into two layers,the genetic algorithm and nonlinear optimization are used to solve.The operational process of genetic algorithm is improved,the fitness function is adjusted,the cross and mutation method are improved,simulated annealing algorithm is combined with,and restrictions are satisfied,infeasible solutions are reduced,optimum performance of genetic algorithm is enhanced.Optimization results show that the algorithm is efficient.

Key concepts: Mathematical optimization, Simulated annealing, Genetic algorithm, Meta-optimization, Computer science, Fitness function, Topology optimization, Algorithm

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