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oaioai:ojs.pkp.sfu.ca:article/603

IMPROVED PARTICLE SWARM OPTIMIZATION FOR NON-LINEAR PROGRAMMING PROBLEM WITH BARRIER METHOD

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Publication date
30 November 2017
Publisher
'GIAP Journals'
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    Abstract

    The Non-Linear Programming Problems (NLPP) are computationally hard to solve as compared to the Linear Programming Problems (LPP). To solve NLPP, the available methods are Lagrangian Multipliers, Sub gradient method, Karush-Kuhn-Tucker conditions, Penalty and Barrier method etc. In this paper, we are applying Barrier method to convert the NLPP with equality constraint to an NLPP without constraint. We use the improved version of famous Particle Swarm Optimization (PSO) method to obtain the solution of NLPP without constraint. SCILAB programming language is used to evaluate the solution on sample problems. The results of sample problems are compared on Improved PSO and general PSO

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    Gyandhara International Academic Publication (GIAP): Journals

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    oaioai:ojs.pkp.sfu.ca:article/603
    Last time updated on 15/12/2019

    This paper was published in Gyandhara International Academic Publication (GIAP): Journals.

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