Multi-Population Parallel Imperialist Competitive Algorithm for Solving Systems of Nonlinear Equation

Amin Majd, Mahdi Abdollahi, Golnaz Sahebi, Davoud Abdollahi, Masoud Daneshtalab, Juha Plosila, Hannu Tenhunen

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    4 Citations (Scopus)

    Abstract

    The widespread importance of optimization and solving NP-hard problems, like solving systems of nonlinear equations, is indisputable in a diverse range of sciences. Vast uses of non-linear equations are undeniable. Some of their applications are in economics, engineering, chemistry, mechanics, medicine, and robotics. There are different types of methods of solving the systems of nonlinear equations. One of the most popular of them is Evolutionary Computing (EC). This paper presents an evolutionary algorithm that is called Parallel Imperialist Competitive Algorithm (PICA) which is based on a multi-population technique for solving systems of nonlinear equations. In order to demonstrate the efficiency of the proposed approach, some well-known problems are utilized. The results indicate that the PICA has a high success and a quick convergence rate.

    Original languageUndefined/Unknown
    Title of host publication2016 International Conference on High Performance Computing & Simulation (HPCS)
    PublisherIEEE
    Pages
    DOIs
    Publication statusPublished - 2016
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on High Performance Computing & Simulation (HPCS) - 2016 International Conference on High Performance Computing & Simulation (HPCS)
    Duration: 18 May 201622 Jul 2016

    Conference

    ConferenceInternational Conference on High Performance Computing & Simulation (HPCS)
    Period18/05/1622/07/16

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