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An Entropy-Balanced Orthogonal Learning Bamboo Forest Growth Optimization Algorithm with Quasi-Affine Transformation

Jeng-Shyang Pan1,2, Xin-Yi Zhang1, Shu-Chuan Chu1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

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Summary
This summary is machine-generated.

A new algorithm, orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO), enhances searchability and global development for optimization problems. It outperforms existing methods in benchmark tests and vehicle routing challenges.

Keywords:
bamboo forest growth optimization algorithmcapacitated vehicle routing problemheuristic optimization algorithmorthogonal learningquasi-affine transformation evolution

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Heuristic Computing

Background:

  • The bamboo forest growth optimization (BFGO) algorithm demonstrates effectiveness but lacks strong exploitation capabilities.
  • Existing optimization algorithms often face challenges in balancing exploration and exploitation for complex problems.

Purpose of the Study:

  • To introduce the orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO) algorithm.
  • To enhance the searchability and global development capabilities of the BFGO algorithm.
  • To evaluate the performance of OQBFGO against other established algorithms and its application to the capacitated vehicle routing problem.

Main Methods:

  • The OQBFGO algorithm integrates quasi-affine transformation evolution to broaden particle distribution and improve searchability via entropy increase.
  • An orthogonal learning strategy is employed to refine particle aggregation from chaotic states, facilitating global development through entropy reduction.
  • Performance evaluation involved testing OQBFGO, BFGO, QBFGO, OBFGO, and three other mature algorithms on the CEC2017 benchmark function.

Main Results:

  • The OQBFGO algorithm demonstrated superior performance compared to BFGO, QBFGO, OBFGO, and three other benchmark algorithms on the CEC2017 test suite.
  • Application of OQBFGO to the capacitated vehicle routing problem yielded better results than competing algorithms.
  • The proposed enhancements significantly improved particle searchability and global development capabilities.

Conclusions:

  • The OQBFGO algorithm offers a significant advancement in optimization techniques, particularly for problems requiring robust exploration and exploitation.
  • The integration of quasi-affine transformation and orthogonal learning provides a synergistic approach to enhance heuristic search.
  • OQBFGO shows strong potential for solving complex real-world optimization challenges, including the capacitated vehicle routing problem.