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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Improving Pareto Local Search Using Cooperative Parallelism Strategies for Multiobjective Combinatorial Optimization.

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    This study enhances parallel Pareto local search (PPLS) for multiobjective combinatorial optimization problems (MCOPs). New cooperative techniques improve solution quality and search efficiency for complex optimization tasks.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Applied Mathematics

    Background:

    • Pareto local search (PLS) is an extension of local search for multiobjective combinatorial optimization problems (MCOPs).
    • Previous work introduced parallel PLS based on decomposition (PPLS/D) to improve anytime performance.
    • PPLS/D utilizes multiple independent parallel processes to search the solution space.

    Purpose of the Study:

    • To further enhance the performance of PPLS/D by introducing cooperative process techniques.
    • To improve the efficiency and effectiveness of solving NP-hard MCOPs.
    • To optimize the approximation of the Pareto front (PF) and subregion division.

    Main Methods:

    • Introduction of a cooperative search mechanism for sharing high-quality solutions among parallel processes.
    • Implementation of a cooperative subregion-adjusting strategy using a master process to approximate the PF and redivide subregions.
    • Experimental evaluation on three NP-hard MCOPs with up to six objectives using the Tianhe-2 supercomputer.

    Main Results:

    • The proposed cooperative techniques significantly improved the performance of PPLS/D.
    • Effective approximation of the Pareto front and even subregion division were achieved.
    • Experimental results demonstrated the effectiveness of the enhanced PPLS/D on complex MCOPs.

    Conclusions:

    • The novel cooperative search and subregion-adjusting strategies enhance parallel Pareto local search for MCOPs.
    • The improved PPLS/D offers a more efficient and effective approach to solving complex multiobjective optimization problems.
    • The study validates the proposed techniques on challenging NP-hard MCOPs.