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Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization.

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    This study introduces a cooperative differential evolution (DE) algorithm using multiple populations for multiobjective optimization. The novel approach enhances performance over existing methods by optimizing all objectives collaboratively.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Multiobjective optimization problems (MOPs) present challenges in finding optimal trade-offs.
    • Existing multiobjective evolutionary algorithms (MOEAs) often struggle with complex Pareto front approximation.

    Purpose of the Study:

    • To propose a novel cooperative differential evolution (DE) algorithm with multiple populations for solving MOPs.
    • To enhance the search capability and Pareto front coverage of DE-based MOP solvers.

    Main Methods:

    • A cooperative framework with M single-objective subpopulations and one archive population for M-objective problems.
    • Adaptive DE applied to each subpopulation and the archive population for parallel and guided search.
    • Utilizing the archive population to maintain nondominated solutions and guide subpopulations.

    Main Results:

    • The proposed cooperative DE algorithm demonstrates superior performance compared to state-of-the-art MOEAs on benchmark problems with 2, 3, and many objectives.
    • Effective approximation of the entire Pareto front is achieved through population cooperation.
    • Online search behavior and parameter sensitivity were analyzed.

    Conclusions:

    • The cooperative DE algorithm effectively addresses multiobjective optimization challenges.
    • The multi-population strategy enhances the exploration and exploitation balance.
    • The algorithm offers a promising alternative for complex MOPs.