2003•Journal of Hydraulic EngineeringRequires access

New method for solving multi-objective problem based on genetic algorithm

You Jin

Open publisher page 13 citations

Abstract

A new multiobjective genetic algorithm based on the variety of plentiful solution in every generation of the evolution is proposed, by which the Pareto set can be searched by calculation of only one time interchange.The appraising function in this method ranks the gene by comparing their performance in each objective function through the sorting matrix created by the objective function. In order to elevate the efficiency of this method, the parameter's calibration is improved and the Pareto solution's selection is controlled effectively.The application in a multiobjective reservoir's longterm optimal operation for power generation and water supply is given as an example to demonstrate the feasibility of this method.

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What this paper is about

A new multiobjective genetic algorithm based on the variety of plentiful solution in every generation of the evolution is proposed, by which the Pareto set can be searched by calculation of only one time interchange.The appraising function in this method ranks the gene by comparing their performance in each objective function through the sorting matrix created by the objective function. In order to elevate the efficiency of this method, the parameter's calibration is improved and the Pareto solution's selection is controlled effectively.The application in a multiobjective reservoir's longterm optimal operation for power generation and water supply is given as an example to demonstrate the feasibility of this method.

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OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A new multiobjective genetic algorithm based on the variety of plentiful solution in every generation of the evolution is proposed, by which the Pareto set can be searched by calculation of only one time interchange.The appraising function in this method ranks the gene by comparing their performance in each objective function through the sorting matrix created by the objective function. In order to elevate the efficiency of this method, the parameter's calibration is improved and the Pareto solution's selection is controlled effectively.The application in a multiobjective reservoir's longterm optimal operation for power generation and water supply is given as an example to demonstrate the feasibility of this method.

Key concepts: Sorting, Mathematical optimization, Genetic algorithm, Selection (genetic algorithm), Pareto principle, Set (abstract data type), Pareto optimal, Multi-objective optimization

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