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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications.

Deyu Tang1,2, Jie Zhao3, Jin Yang1

  • 1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

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This study introduces an enhanced shuffled frog leaping algorithm (SFLA) using quantum and eigenvector evolution for improved optimization. The novel SFLA demonstrates superior performance over existing heuristic algorithms in benchmark tests.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Heuristic methods

Background:

  • The shuffled frog leaping algorithm (SFLA) is a heuristic optimization method inspired by frog foraging behavior.
  • Existing SFLA improvements focus on local search within the particle swarm optimization (PSO) framework, limiting overall development.
  • A need exists for SFLA enhancements that address both local and global search capabilities.

Purpose of the Study:

  • To propose a novel SFLA scheme incorporating evolutionary strategies.
  • To enhance both local and global search abilities of the SFLA.
  • To evaluate the performance of the proposed SFLA against established algorithms.

Main Methods:

  • Developed a new scheme using quantum evolution for local search with historical information and two potential wells.
  • Implemented eigenvector evolution with an evolutionary operator for global search.
  • Tested the algorithm on CEC2013, CEC2014 benchmark suites, and a Support Vector Machine (SVM) parameter optimization problem.

Main Results:

  • The proposed SFLA significantly outperformed 15 well-known heuristic algorithms.
  • Experimental results validated the effectiveness of quantum and eigenvector evolution in enhancing SFLA performance.
  • The algorithm showed improved convergence speed and effectiveness in tested optimization problems.

Conclusions:

  • The novel SFLA scheme effectively improves upon existing methods.
  • Quantum and eigenvector evolution are promising strategies for advancing heuristic search algorithms.
  • The enhanced SFLA offers a superior alternative for complex optimization tasks.