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Micro Multiobjective Evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial

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    This study introduces a micro multiobjective optimization evolutionary algorithm (μ MOEA) for embedded systems. This efficient algorithm optimizes industrial processes on resource-constrained processors, demonstrating successful applications in grinding and micro-grid energy management.

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    Area of Science:

    • Engineering
    • Computer Science
    • Optimization

    Background:

    • Embedded systems in industrial applications often require offline multiobjective optimization.
    • Conventional multiobjective optimization evolutionary algorithms (MOEAs) are computationally intensive and unsuitable for resource-limited embedded processors.

    Purpose of the Study:

    • To propose a novel, lightweight MOEA (μ MOEA) optimized for embedded systems.
    • To enhance the efficiency and resource utilization of MOEAs on embedded processors.

    Main Methods:

    • Development of a micro MOEA (μ MOEA) incorporating a piecewise strategy based on the MOEA/D framework.
    • Implementation of a dynamic weight vector update trigger mechanism for optimized resource management.
    • Extensive testing on benchmark problems (ZDT, DTLZ, SMOP, MaF) and industrial simulations.

    Main Results:

    • μ MOEA demonstrates superior performance on various artificial test problems.
    • The algorithm effectively handles the computational and memory constraints of embedded processors.
    • Successful simulation of μ MOEA in semi-autogenous grinding and micro-grid energy optimization.

    Conclusions:

    • μ MOEA is a feasible and effective approach for multiobjective optimization in embedded industrial applications.
    • The proposed algorithm offers a practical solution for resource-constrained optimization tasks.
    • This research validates the application of MOEAs on embedded processors for real-world industrial challenges.