Surfing on fitness landscapes: a boost on optimization by Fourier surrogate modeling

Luca Manzoni (Corresponding author), Daniele M. Papetti, Paolo Cazzaniga, Simone Spolaor, Giancarlo Mauri, Daniela Besozzi, Marco S. Nobile (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
21 Downloads (Pure)

Abstract

Surfing in rough waters is not always as fun as wave riding the “big one”. Similarly, in optimization problems, fitness landscapes with a huge number of local optima make the search for the global optimum a hard and generally annoying game. Computational Intelligence optimization metaheuristics use a set of individuals that “surf” across the fitness landscape, sharing and exploiting pieces of information about local fitness values in a joint effort to find out the global optimum. In this context, we designed surF, a novel surrogate modeling technique that leverages the discrete Fourier transform to generate a smoother, and possibly easier to explore, fitness landscape. The rationale behind this idea is that filtering out the high frequencies of the fitness function and keeping only its partial information (i.e., the low frequencies) can actually be beneficial in the optimization process. We prove our theory by combining surF with a settings free variant of Particle Swarm Optimization (PSO) based on Fuzzy Logic, called Fuzzy Self-Tuning PSO. Specifically, we introduce a new algorithm, named F3ST-PSO, which performs a preliminary exploration on the surrogate model followed by a second optimization using the actual fitness function. We show that F3ST-PSO can lead to improved performances, notably using the same budget of fitness evaluations.
Original languageEnglish
Article number285
Number of pages17
JournalEntropy
Volume22
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • global optimization
  • particle swarm optimization
  • fuzzy self-tuning pso
  • Fourier transform
  • surrogate modeling
  • Global optimization
  • Fuzzy self-tuning PSO
  • Surrogate modeling
  • Particle swarm optimization

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