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    This study introduces segment-based search (SBS) to enhance evolutionary algorithms for continuous multiobjective optimization. SBS improves algorithm performance by intelligently guiding search through segmented spaces.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Optimization

    Background:

    • Evolutionary algorithms (EAs) are widely used for complex optimization tasks.
    • Continuous multiobjective optimization problems (MOPs) present significant challenges due to competing objectives.
    • Existing EAs often struggle with efficient exploration and exploitation in MOPs.

    Purpose of the Study:

    • To propose a novel variation operator, segment-based search (SBS), for improving EA performance on continuous MOPs.
    • To investigate the adaptive nature and effectiveness of SBS in guiding evolutionary processes.
    • To analyze the impact of SBS parameters and compare it against existing operators.

    Main Methods:

    • Developed segment-based search (SBS) operator, dividing the search space into segments based on evolutionary feedback.
    • Implemented micro-jumping and macro-jumping operations within segments for information exchange.
    • Designed SBS to activate adaptively during slow population evolution, complementing standard genetic operators.
    • Conducted experiments on 36 test problems, analyzing parameter sensitivities and performing comparative studies.

    Main Results:

    • Experimental results demonstrate that incorporating SBS significantly improves the performance of evolutionary algorithms on continuous MOPs.
    • Analysis revealed the influence of algorithm settings like dimensionality and boundary relaxation on SBS effectiveness.
    • Comparative studies confirmed the advantages of SBS over three representative variation operators.

    Conclusions:

    • Segment-based search (SBS) is an effective variation operator for enhancing evolutionary algorithms in continuous multiobjective optimization.
    • The adaptive and segmented approach of SBS facilitates more efficient information exchange and search guidance.
    • SBS offers a promising direction for advancing the capabilities of evolutionary computation for complex optimization problems.