2008Unpublished venueRequires access

New penalty function with differential evolution for constrained optimization

Changshou Deng, Changyong Liang, Bingyan Zhao, Anyuan Deng

Open publisher page 8 citations

Abstract

The penalty function is one of the most commonly used approaches for constrained optimization problems. However, it often leads to additional parameters and the parameters are not easy for the users to select. A new way without additional parameters to deal the constrained optimizations was proposed. Firstly, a new penalty function was defined using the constrained functions without additional parameters. Secondly, combining the penalty function and the original objective function, a new objective function without any constrained conditions was got. Then Differential Evolution algorithm was used to solve the non-constrained optimization problem. The numerical experiments show its advantage over the other existing method.

About this research paper

What this paper is about

The penalty function is one of the most commonly used approaches for constrained optimization problems. However, it often leads to additional parameters and the parameters are not easy for the users to select. A new way without additional parameters to deal the constrained optimizations was proposed. Firstly, a new penalty function was defined using the constrained functions without additional parameters. Secondly, combining the penalty function and the original objective function, a new objective function without any constrained conditions was got. Then Differential Evolution algorithm was used to solve the non-constrained optimization problem. The numerical experiments show its advantage over the other existing method.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The penalty function is one of the most commonly used approaches for constrained optimization problems. However, it often leads to additional parameters and the parameters are not easy for the users to select. A new way without additional parameters to deal the constrained optimizations was proposed. Firstly, a new penalty function was defined using the constrained functions without additional parameters. Secondly, combining the penalty function and the original objective function, a new objective function without any constrained conditions was got. Then Differential Evolution algorithm was used to solve the non-constrained optimization problem. The numerical experiments show its advantage over the other existing method.

Key concepts: Penalty method, Mathematical optimization, Constrained optimization, Constrained optimization problem, Computer science, Function (biology), Differential evolution, Optimization problem

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