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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An Indicator-Based Many-Objective Evolutionary Algorithm With Boundary Protection.

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    Many-objective optimization problems are challenging for traditional algorithms. A new method, MaOEA-IBP, uses an Iϵ indicator and boundary protection to improve population diversity and coverage for better optimization results.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Many-objective optimization problems (MaOPs) challenge traditional Pareto-based multiobjective evolutionary algorithms (MOEAs) due to exploding solution spaces and loss of selection pressure.
    • Indicator-based MaOEAs enhance environmental selection but often struggle with maintaining population diversity and coverage.

    Purpose of the Study:

    • To introduce MaOEA-IBP, a novel indicator-based many-objective evolutionary algorithm designed to improve population convergence, diversity, and coverage.
    • To address the limitations of existing indicator-based MaOEAs in maintaining population quality for MaOPs.

    Main Methods:

    • Developed MaOEA-IBP incorporating a worst elimination mechanism based on the Iϵ indicator.
    • Implemented a boundary protection strategy to enhance the balance between convergence, diversity, and coverage.
    • Identified solutions with the smallest Iϵ value for elimination, using dominance or boundary protection when necessary.

    Main Results:

    • MaOEA-IBP demonstrated competitive performance against four indicator-based and five state-of-the-art MaOEAs on benchmark MaOPs.
    • The proposed algorithm effectively balances population convergence, diversity, and coverage.

    Conclusions:

    • MaOEA-IBP offers a promising approach to enhance the performance of indicator-based algorithms for many-objective optimization.
    • The integration of Iϵ indicator-based worst elimination and boundary protection effectively tackles the challenges posed by MaOPs.