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A Variable Search Space Strategy Based on Sequential Trust Region Determination Technique.

Qinqin Fan, Xuefeng Yan, Yilian Zhang

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    This study introduces a variable search space (VSS) strategy to improve metaheuristic algorithms. By sequentially determining a trust region, VSS enhances solution precision for complex optimization problems.

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    Area of Science:

    • Optimization algorithms
    • Computational intelligence
    • Metaheuristic optimization

    Background:

    • Optimization problem complexity is defined by decision and objective spaces.
    • Research has primarily focused on objective space improvements for metaheuristics, neglecting the decision space.
    • Metaheuristic algorithms may struggle to find optimal solutions within the entire feasible region.

    Purpose of the Study:

    • To propose a variable search space (VSS) strategy to enhance metaheuristic algorithm performance.
    • To address the challenge of suboptimal results in large feasible regions.
    • To investigate the effectiveness of reducing the search space by finding a trust region.

    Main Methods:

    • A variable search space (VSS) strategy based on sequential trust region determination is introduced.
    • The VSS divides the optimization into two stages: trust region identification and solution search within the region.
    • The approach was evaluated on IEEE CEC2014 and BBOB2012 test suites.

    Main Results:

    • The VSS strategy was effective in improving the performance of metaheuristic algorithms.
    • Locating a trust region significantly enhanced solution precision, particularly for complex problems.
    • The sequential determination of trust domains for each variable proved beneficial.

    Conclusions:

    • Reducing the search space through a trust region is a viable method to improve metaheuristic algorithm convergence and precision.
    • The proposed VSS strategy offers a promising approach for tackling complex optimization challenges.
    • Further research into trust region determination can lead to more robust optimization techniques.