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A penalty function approach for solving inequality constrained optimization problems

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TLDR
In this paper, a computational approach based on an exterior penalty function MPQI method is given for solving a class of continuous inequality constrained optimization problems, and the essential steps of the partial quadratic interpolation technique and its modified are given.
Abstract
I n this paper, a computational approach based on an exterior penalty function MPQI method is given for solving a class of continuous inequality constrained optimization problems. The essential steps of the partial quadratic interpolation technique and its modified are given. The numerical algorithm and the flowchart of the penalty function method combined with the Modified Partial Quadratic Interpolation Technique are given. For illustration, three examples are solved using the proposed method. From the solutions obtained, we observe that the values of their object functions are amongst the smallest when compared with those obtained by other existing methods available in the literature. More importantly, our method finds solution which satisfies the continuous inequality constraints.

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References
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Book

Engineering Optimization : Theory and Practice

TL;DR: This chapter discusses Optimization Techniques, which are used in Linear Programming I and II, and Nonlinear Programming II, which is concerned with One-Dimensional Minimization.
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Optimization Theory and Applications

TL;DR: This book discusses the construction of mental ray, a model for synthetic lighting, and some of the techniques used to design and implement such models.
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Penalty Function Methods for Constrained Optimization with Genetic Algorithms

TL;DR: These penalty-based methods for handling constraints in Genetic Algorithms are presented and discussed and their strengths and weaknesses are discussed.
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A new exact penalty function method for continuous inequality constrained optimization problems

TL;DR: In this article, a computational approach based on a new exact penalty refinement method is devised for solving a class of continuous======INEquality constrained optimization problems, where the continuous======inequality constraints are first approximated by smooth function in======�-integral form.
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A computational study on different penalty approaches for solving constrained global optimization problems with the electromagnetism-like method

TL;DR: It is shown that EM is a suitable alternative to these methods and that it has a role to play in solving constrained global optimization problems.
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