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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

On local search for bi-objective knapsack problems.

Arnaud Liefooghe1, Luís Paquete, José Rui Figueira

  • 1LIFL, Université Lille 1, UMR CNRS 8022, 59655 Villeneuve d'Ascq cedex, France. arnaud.liefooghe@univ-lille1.fr

Evolutionary Computation
|March 6, 2012
PubMed
Summary
This summary is machine-generated.

A new local search method efficiently solves the bi-objective binary knapsack problem. This approach finds optimal solutions faster than exact algorithms, maximizing profit and minimizing weight.

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Last Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Operations Research
  • Combinatorial Optimization
  • Algorithm Design

Background:

  • The bi-objective binary knapsack problem involves maximizing profit and minimizing weight simultaneously.
  • Existing exact algorithms can be computationally intensive for large problem instances.

Purpose of the Study:

  • To propose and evaluate a local search approach for the bi-objective binary knapsack problem.
  • To investigate the structural properties of the efficient set for this problem.

Main Methods:

  • An experimental study on the connectedness property of the efficient set.
  • Development of a local search algorithm based on the identified property.
  • Comparison of the local search algorithm against exact algorithms using runtime and solution quality metrics.

Main Results:

  • The local search algorithm effectively identifies a representative set of optimal solutions in most cases.
  • The proposed algorithm significantly outperforms exact algorithms in terms of runtime.
  • The algorithm's performance is robust across different variants of the problem.

Conclusions:

  • A simple local search approach is a highly efficient method for solving the bi-objective binary knapsack problem.
  • This method provides a practical alternative to exact algorithms, especially for large-scale instances.
  • The findings highlight the utility of exploiting structural properties of the efficient set in algorithm design.