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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Published on: December 9, 2012

An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical

Sk Minhazul Islam1, Swagatam Das, Saurav Ghosh

  • 1Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India. skminha.isl@gmail.com

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Summary
This summary is machine-generated.

This study introduces novel mutation and crossover strategies for Differential Evolution (DE), enhancing its performance in real parameter optimization. These improvements lead to statistically superior results on benchmark problems.

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Differential Evolution (DE) is a powerful stochastic optimization algorithm for real parameters.
  • Existing DE variants face challenges in achieving optimal performance across diverse problems.

Purpose of the Study:

  • To propose and evaluate new mutation and crossover strategies for Differential Evolution (DE).
  • To develop an adaptive parameter control scheme for DE.
  • To enhance the overall performance and robustness of DE algorithms.

Main Methods:

  • Introduced a new mutation operator, DE/current-to-gr_best/1, using a group best vector.
  • Implemented a fitness-induced parent selection scheme for binomial crossover.
  • Developed an adaptive strategy for DE's control parameters.
  • Compared the proposed DE variant against classical and state-of-the-art DE algorithms on 25 benchmark problems.

Main Results:

  • The proposed DE variant demonstrated significant performance improvements.
  • Achieved statistical superiority over existing state-of-the-art DE variants on a wide range of test problems.
  • The novel strategies enhanced the performance of established DE variants like jDE and JADE.

Conclusions:

  • The integrated proposed strategies substantially improve DE's effectiveness in real parameter optimization.
  • The enhanced DE variant offers a robust and high-performing alternative for complex optimization tasks.
  • Further integration of these strategies can boost the performance of other advanced DE algorithms.