Pseudo derivative evolutionary algorithm and convergence analysis

Yang Yu, Zhongjie Wang, Huaglory Tianfield, Chengchao Lu

    Research output: Contribution to journalArticle

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    Abstract

    In this paper a novel evolutionary algorithm (EA), called pseudo derivative evolutionary algorithm (PDEA), is proposed. The basic idea of PDEA is to use pseudo derivative, which is obtained based on the information produced during the evolution, to help search the solution of optimization problem. The pseudo derivative drives the search process in a more informed direction. That makes PDEA different from the random optimization methods. The convergence of PDEA is first analyzed based on systems theory. The convergence condition of PDEA is then derived, though this condition is too strong to be satisfied. Next, this condition is relaxed based on entropy theory. Finally, performances of PDEA are evaluated on the benchmark functions and an adaptive liquid level control system of a surge tank. The numeric simulation results show that PDEA is capable of finding the solutions to the optimization problems with good accuracy, reliability, and speed.
    Original languageEnglish
    Number of pages7
    JournalInternational Journal of Modeling, Simulation, and Scientific Computing
    DOIs
    Publication statusPublished - 6 Jul 2018

    Fingerprint

    Algorithm Analysis
    Convergence Analysis
    Evolutionary algorithms
    Evolutionary Algorithms
    Derivatives
    Derivative
    Surge tanks
    Optimization Problem
    Convergence Condition
    Surge
    Level control
    System theory
    Systems Theory
    Numerics
    Optimization Methods
    Entropy
    Control System
    Liquid
    Benchmark
    Control systems

    Keywords

    • pseudo derivative evolutionary algorithm
    • evolutionary algorithm
    • convergence analysis
    • entropy theory

    Cite this

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    title = "Pseudo derivative evolutionary algorithm and convergence analysis",
    abstract = "In this paper a novel evolutionary algorithm (EA), called pseudo derivative evolutionary algorithm (PDEA), is proposed. The basic idea of PDEA is to use pseudo derivative, which is obtained based on the information produced during the evolution, to help search the solution of optimization problem. The pseudo derivative drives the search process in a more informed direction. That makes PDEA different from the random optimization methods. The convergence of PDEA is first analyzed based on systems theory. The convergence condition of PDEA is then derived, though this condition is too strong to be satisfied. Next, this condition is relaxed based on entropy theory. Finally, performances of PDEA are evaluated on the benchmark functions and an adaptive liquid level control system of a surge tank. The numeric simulation results show that PDEA is capable of finding the solutions to the optimization problems with good accuracy, reliability, and speed.",
    keywords = "pseudo derivative evolutionary algorithm , evolutionary algorithm , convergence analysis, entropy theory",
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    note = "Acceptance in SAN AAM: 12m embargo",
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    Pseudo derivative evolutionary algorithm and convergence analysis. / Yu, Yang; Wang, Zhongjie; Tianfield, Huaglory; Lu, Chengchao.

    In: International Journal of Modeling, Simulation, and Scientific Computing, 06.07.2018.

    Research output: Contribution to journalArticle

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    T1 - Pseudo derivative evolutionary algorithm and convergence analysis

    AU - Yu, Yang

    AU - Wang, Zhongjie

    AU - Tianfield, Huaglory

    AU - Lu, Chengchao

    N1 - Acceptance in SAN AAM: 12m embargo

    PY - 2018/7/6

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    AB - In this paper a novel evolutionary algorithm (EA), called pseudo derivative evolutionary algorithm (PDEA), is proposed. The basic idea of PDEA is to use pseudo derivative, which is obtained based on the information produced during the evolution, to help search the solution of optimization problem. The pseudo derivative drives the search process in a more informed direction. That makes PDEA different from the random optimization methods. The convergence of PDEA is first analyzed based on systems theory. The convergence condition of PDEA is then derived, though this condition is too strong to be satisfied. Next, this condition is relaxed based on entropy theory. Finally, performances of PDEA are evaluated on the benchmark functions and an adaptive liquid level control system of a surge tank. The numeric simulation results show that PDEA is capable of finding the solutions to the optimization problems with good accuracy, reliability, and speed.

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