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Evolutionary Algorithm for Knee-Based Multiple Criteria Decision Making.

Kai Zhang, Gary G Yen, Zhenan He

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    Summary
    This summary is machine-generated.

    This study introduces EvoKnee, a recursive evolutionary algorithm that efficiently finds knee solutions, reducing computational cost and decision-making burden in multiobjective optimization problems.

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    Area of Science:

    • Computational intelligence
    • Optimization algorithms
    • Evolutionary computation

    Background:

    • Traditional multiobjective evolutionary algorithms are computationally expensive and yield numerous Pareto optimal solutions.
    • Selecting a single solution from a large set poses a significant challenge for decision-makers, especially with increasing complexity.

    Purpose of the Study:

    • To propose a novel recursive evolutionary algorithm, EvoKnee, for directly identifying global and local knee solutions.
    • To reduce the computational cost and decision-maker burden associated with multiobjective optimization.

    Main Methods:

    • EvoKnee utilizes the minimum Manhattan distance approach to identify knee solutions.
    • The algorithm preserves only rank-one nondominated solutions and employs Boundary Individuals Selection (M^2 individuals).
    • A graphical user interface simulator was developed for experimental validation.

    Main Results:

    • EvoKnee significantly reduces computational cost by maintaining a small population size.
    • The algorithm efficiently converges to both global and local knee solutions.
    • Evaluations on benchmark functions (DO2DK, DEB2DK, DEB2DK2, DEB3DK) confirm EvoKnee's effectiveness.

    Conclusions:

    • EvoKnee offers a computationally efficient and effective method for finding knee solutions in multiobjective optimization.
    • The algorithm alleviates the challenge of selecting a single solution from a vast Pareto front.
    • EvoKnee demonstrates superior performance in identifying all global and local knee solutions.