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An improved farmland fertility algorithm for many-objective optimization problems.

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This study introduces a new many-objective optimization algorithm (MOIFF) to enhance evolutionary algorithms for complex problems. MOIFF improves convergence and diversity, addressing scalability challenges in many-objective optimization.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Many-objective optimization problems (MaOPs) present scalability challenges for traditional evolutionary algorithms.
  • Existing algorithms struggle with leader selection and performance as the number of objectives increases.

Purpose of the Study:

  • To propose a novel many-objective evolutionary algorithm (MOIFF) for improved performance on MaOPs.
  • To enhance the Farmland Fertility algorithm (FF) for effective handling of multiple objectives.

Main Methods:

  • Developed MOIFF based on the bio-inspired Farmland Fertility algorithm (FF).
  • Introduced a cumulative ranking value for fitness assessment and memory updates.
  • Implemented hybrid subspace and full space search strategies.
  • Incorporated adaptive environmental selection.

Main Results:

  • MOIFF demonstrated competitive convergence and diversity on MaOPs.
  • The algorithm was tested against four state-of-the-art algorithms on DTLZ and WFG test suites.
  • Experimental results validate the effectiveness of the proposed enhancements.

Conclusions:

  • MOIFF offers a promising approach to address the scalability and performance limitations of evolutionary algorithms in MaOPs.
  • The tailored FF strategy effectively handles the complexities of many-objective optimization.