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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Multilevel learning aided coevolutionary particle swarm optimization algorithm for multiobjective fuzzy flexible job

Juan Chen1, Hong Zhao2, Zhiya Cui2

  • 1Anhui Jianzhu University, Hefei, 230000, China.

Scientific Reports
|November 6, 2025
PubMed
Summary
This summary is machine-generated.

A new Multi-Level Learning-aided Co-evolutionary Particle Swarm Optimization (MLL-CPSO) algorithm effectively solves complex multi-objective fuzzy flexible job shop scheduling problems (MofFJSPs). It enhances convergence speed and avoids local optima for better optimization results.

Keywords:
Multi-level learning-aidedMulti-objective evolutionary algorithmsMulti-objective fuzzy flexible job shop scheduling problemParticle swarm optimization

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Systems Engineering

Background:

  • Multi-objective fuzzy Flexible Job shop Scheduling Problems (MofFJSPs) involve optimizing conflicting fuzzy objectives under realistic constraints.
  • Existing algorithms struggle with local optima and slow convergence due to the fuzzy and conflicting nature of MofFJSP objectives.

Purpose of the Study:

  • To propose an efficient and effective algorithm for solving MofFJSPs.
  • To address the challenges of local optima and slow convergence in MofFJSP optimization.

Main Methods:

  • A novel Multi-Level Learning-aided Co-evolutionary Particle Swarm Optimization (MLL-CPSO) algorithm is introduced.
  • Key components include a Multi-Level Learning (MLL) strategy, Simulated Annealing-based Strengthening Diversity (SASD), and a Co-evolutionary Information Update (CeIU) mechanism.
  • MLL hierarchically learns evolutionary information to avoid local optima and accelerate convergence towards Pareto optima.

Main Results:

  • The MLL-CPSO algorithm demonstrated superior performance compared to seven state-of-the-art algorithms.
  • Experiments were conducted on 3 typical benchmarks comprising 23 instances.
  • The proposed algorithm consistently outperformed others across most test environments.

Conclusions:

  • MLL-CPSO offers an effective solution for MofFJSPs, overcoming limitations of existing methods.
  • The algorithm's novel strategies enhance global search ability and improve the quality of co-evolutionary information.
  • This approach facilitates faster exploration of Pareto optimal solutions in complex scheduling scenarios.