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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Modified differential evolution algorithm with onlooker bee operator for mixed discrete-continuous optimization.

Yongfei Miao1, Qinghua Su2, Zhongbo Hu2

  • 1School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070 People's Republic of China.

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|November 22, 2016
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Summary
This summary is machine-generated.

This study introduces two enhanced differential evolution (DE) algorithms for mixed discrete-continuous non-linear programming. These novel algorithms improve global exploration, with one modified DE algorithm finding new optima for engineering problems.

Keywords:
Artificial bee colony algorithmDesign of coil spring problemDifferential evolution Algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Operations Research

Background:

  • Non-linear programming problems with mixed discrete and continuous variables present significant computational challenges.
  • Existing differential evolution (DE) algorithms like DE/best/1 and DE/cur-to-best/1 can suffer from unbalanced searching and limited global exploration.

Purpose of the Study:

  • To propose two modified differential evolution algorithms designed to solve non-linear programming problems with mixed discrete and continuous variables.
  • To enhance the global exploration capability of promising individuals within the DE framework.

Main Methods:

  • Two modified algorithms based on differential evolution (DE) are developed.
  • A novel random search strategy, inspired by the artificial bee colony algorithm, is incorporated into DE/best/1 and DE/cur-to-best/1.
  • Numerical experiments are conducted using the CEC2005 benchmark function set and two mixed discrete-continuous engineering optimization problems.

Main Results:

  • The modified algorithms demonstrate improved effectiveness in solving non-linear programming problems.
  • Experiment 1 verified the effectiveness of the enhanced random search strategy on benchmark functions.
  • Experiment 2 illustrated the competitiveness and practicality of the proposed algorithms, with the modified DE/cur-to-best/1 achieving new optimal solutions for two engineering problems.

Conclusions:

  • The proposed modified DE algorithms effectively address the challenges of mixed discrete-continuous non-linear programming.
  • The novel random search strategy enhances global exploration and overcomes the searching unbalance of conventional DE variants.
  • The modified DE/cur-to-best/1 algorithm shows significant potential for practical engineering optimization applications.