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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Shekoofeh Haghdoost1, Mohammad Hossein Niksokhan1, Mohammad G Zamani2

  • 1Faculty of Environment, University of Tehran, Tehran, Iran.

Environmental Science and Pollution Research International
|November 27, 2023
PubMed
Summary
This summary is machine-generated.

A new optimization model, the multi-objective cuckoo optimization algorithm (MOCOA), outperforms existing methods for river water quality management. MOCOA effectively allocates waste loads, achieving better results in violation index and inequity values for cleaner river systems.

Keywords:
Cuckoo optimization algorithm (COA)Multi-objective optimization (MOO)Non-dominated sorting genetic algorithm (NSGA-II)Pareto frontWaste load allocation (WLA)

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Computational Intelligence

Background:

  • River water pollution is a growing concern due to increasing waste discharge and limited self-cleaning capacities.
  • Existing methods like the non-dominated sorting genetic algorithm-II (NSGA-II) have shown effectiveness in river water quality management.
  • There is a need for novel optimization models to address complex waste load allocation challenges.

Purpose of the Study:

  • To introduce and evaluate a new optimization framework, the multi-objective cuckoo optimization algorithm (MOCOA), for river water quality management.
  • To compare the performance of MOCOA against NSGA-II in a waste load allocation problem.
  • To assess MOCOA's ability to balance discharge goals with environmental protection.

Main Methods:

  • Developed a new optimization framework incorporating non-dominated sorting and ranking selection.
  • Implemented the MOCOA for a point-source waste load allocation issue in the Jajrood River, Iran.
  • Linked a simulation model with a hybrid optimization model (cuckoo optimization algorithm + NSGA-II) to create a multi-objective algorithm.

Main Results:

  • MOCOA demonstrated a superior Pareto front compared to NSGA-II in terms of violation index and inequity values.
  • For identical population sizes, MOCOA achieved a significantly better Pareto front range (0.379-2.28) than NSGA-II (1.31-2.36).
  • MOCOA attained optimal equity with a smaller population size, indicating greater efficiency.

Conclusions:

  • MOCOA is an effective and efficient algorithm for waste load allocation in river systems.
  • The proposed MOCOA framework offers significant advantages over NSGA-II for optimizing river water quality.
  • This research provides a valuable tool for environmental authorities in managing river pollution.