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Updated: Sep 25, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An evolutionary algorithm for multi-objective optimization of freshwater consumption in textile dyeing industry.

Ihsan Elahi1,2, Hamid Ali1, Muhammad Asif1

  • 1Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

A new multi-objective group counseling optimizer (MOGCO-II) improves solution quality and speed. Applied to textile dyeing, it significantly reduces freshwater use by up to 35%.

Keywords:
AlgorithmsEvolutionary algorithmsOptimizationOptimization problemsTextile dyeing industry

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Area of Science:

  • Computational Intelligence
  • Environmental Engineering
  • Operations Research

Background:

  • Multi-objective evolutionary algorithms (MOEAs) face challenges in solution spread and convergence speed.
  • Existing MOEAs often require significant fitness evolution to identify optimal Pareto fronts.
  • The textile dyeing industry consumes substantial freshwater and generates heavily polluted wastewater, causing environmental concerns.

Purpose of the Study:

  • To introduce an enhanced multi-objective group counseling optimizer, MOGCO-II.
  • To evaluate MOGCO-II's performance against established algorithms like MOGCO, MOPSO, MOCLPSO, and NSGA-II.
  • To develop and assess a MOGCO-II based optimization scheduling model for reducing freshwater consumption in textile dyeing.

Main Methods:

  • Developed MOGCO-II, an extended version of the multi-objective group counseling optimizer.
  • Compared MOGCO-II with MOGCO, MOPSO, MOCLPSO, and NSGA-II on benchmark Zitzler-Deb-Thiele (ZDT) functions.
  • Implemented a MOGCO-II based scheduling model for a textile dyeing process.

Main Results:

  • MOGCO-II demonstrated superior solution generation compared to MOGCO, MOPSO, MOCLPSO, and NSGA-II.
  • The proposed algorithm achieved optimal Pareto fronts with a lower fitness evolution value.
  • The MOGCO-II optimization scheduling model reduced freshwater consumption in textile dyeing by up to 35% compared to manual methods.

Conclusions:

  • MOGCO-II offers improved performance in terms of solution quality and convergence efficiency for multi-objective optimization problems.
  • The MOGCO-II based scheduling model presents a viable and effective strategy for sustainable water management in the textile dyeing industry.
  • This research highlights the potential of advanced optimization techniques to address critical environmental challenges in industrial processes.