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  3. Problem Features Versus Algorithm Performance On Rugged Multiobjective Combinatorial Fitness Landscapes.
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  3. Problem Features Versus Algorithm Performance On Rugged Multiobjective Combinatorial Fitness Landscapes.

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Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes.

Fabio Daolio1, Arnaud Liefooghe2, Sébastien Verel3

  • 1Shinshu University, Faculty of Engineering, Nagano, Japan fdaolio@shinshu-u.ac.jp.

Evolutionary Computation
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View abstract on PubMed

Summary
This summary is machine-generated.

This study examines how problem features affect randomized search for multiobjective optimization. Ruggedness and multimodality significantly impact algorithm performance and instance hardness.

Keywords:
Evolutionary multiobjective optimizationblack-box 0–1 multiobjective problemsempirical performance modelingfeature-based analysisfitness landscape and problem difficultymultilevel multivariate analysisrandom-effects mixed models

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Area of Science:

  • Optimization
  • Computer Science
  • Artificial Intelligence

Background:

  • Black-box multiobjective combinatorial optimization problems present significant computational challenges.
  • Understanding the influence of problem features on algorithm performance is crucial for developing effective optimization strategies.

Purpose of the Study:

  • To contrast the impact of problem features on randomized search heuristics for black-box multiobjective combinatorial optimization.
  • To investigate the performance of global evolutionary optimization algorithm with an ergodic variation operator (GSEMO) and neighborhood-based local search heuristic (PLS).
  • To identify key problem features that characterize instance hardness.

Main Methods:

  • Performance measurement of GSEMO and PLS on [Formula: see text]MNK-landscapes with tunable ruggedness, objective space dimension, and objective correlation.
  • Definition and analysis of problem features characterizing the fitness landscape and their association with algorithm runtime.
  • Mixed-effects multilinear regression to assess the effect of problem features on algorithm performance.

    • Main Results:

    • Ruggedness and multimodality were identified as critical factors influencing instance hardness.
    • The study quantified the individual and joint effects of problem features on the performance of both GSEMO and PLS.
    • Intercorrelation between problem features and their association with algorithm runtime were analyzed.

    Conclusions:

    • Ruggedness and multimodality are key determinants of instance hardness in multiobjective optimization.
    • Problem features significantly impact the performance of randomized search heuristics, providing insights for algorithm selection and design.
    • The findings offer a deeper understanding of the relationship between problem characteristics and algorithm efficiency in solving complex optimization tasks.