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    Area of Science:

    • Computer Science
    • Operations Research
    • Network Engineering

    Background:

    • The Set Cover problem is vital for extending wireless sensor network (WSN) lifetime by partitioning sensors into covers.
    • Efficiently covering all targets while maximizing the number of covers is a key challenge.

    Purpose of the Study:

    • To propose a novel memetic algorithm (MA) for the Set Cover problem.
    • To enhance the performance of evolutionary algorithms in WSNs.

    Main Methods:

    • Developed a memetic algorithm (MA) integrating an integer-coded genetic algorithm with local search.
    • Adapted crossover and mutation operators for integer representation.
    • Designed a new fitness function considering cover count and sensor contribution.
    • Introduced a 'recycling operator' for local improvement.

    Main Results:

    • The proposed MA significantly outperformed five other evolutionary algorithms.
    • Achieved higher number of covers and improved hit rate (HR) by 38.1%.
    • Reduced running time by 78.7% compared to state-of-the-art MAs.

    Conclusions:

    • The novel MA is effective and efficient for the Set Cover problem.
    • Demonstrated significant improvements in HR and running time.
    • Validates the MA's potential for practical WSN applications.