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Multi-directional local search.

Fabien Tricoire1

  • 1Department of Business Administration, University of Vienna, Bruenner Strasse 72, 1210 Wien, Austria ; NICTA/UNSW, Sydney, Australia.

Computers & Operations Research
|August 21, 2014
PubMed
Summary
This summary is machine-generated.

This study presents multi-directional local search, a new metaheuristic for multi-objective optimization. It effectively solves complex problems, offering comparable results to existing methods with reasonable computational effort.

Keywords:
MetaheuristicsMulti-objective optimization

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Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • Multi-objective optimization problems (MOPs) are prevalent in various scientific and engineering domains.
  • Existing metaheuristics often face challenges in balancing solution quality and computational cost for MOPs.

Purpose of the Study:

  • To introduce and evaluate a novel metaheuristic called multi-directional local search (MDLS) for solving MOPs.
  • To demonstrate the applicability of MDLS to a range of established multi-objective problems.

Main Methods:

  • Development of an algorithmic framework for the multi-directional local search metaheuristic.
  • Application of MDLS to benchmark instances of the multi-objective multi-dimensional knapsack problem, bi-objective set packing problem, and bi-objective orienteering problem.

Main Results:

  • MDLS systematically generates high-quality solution sets for the tested MOPs.
  • The performance of MDLS is comparable to state-of-the-art methods on benchmark instances.
  • The method achieves these results within acceptable CPU time constraints.

Conclusions:

  • The proposed multi-directional local search framework is a viable and effective approach for tackling multi-objective optimization challenges.
  • MDLS offers a promising alternative for researchers and practitioners dealing with complex optimization tasks.