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Key factors influencing performance of simulated annealing optimization

Published on:

8 February 2024

Primary Category:

Neural and Evolutionary Computing

Paper Authors:

Pedro A. Castillo,

Maribel García Arenas,

Nuria Rico,

Antonio Miguel Mora,

Pablo García-Sánchez,

Juan Luis Jiménez Laredo,

Juan Julián Merelo Guervós


Key Details

Analyzed impact of 5 key parameters on simulated annealing variant

Cooling schedule and initial temperature significantly influence results

Higher iteration counts improve exploration and fitness

Population size not significant, confirming single-state approach

Verified optimal settings match theoretical analysis

AI generated summary

Key factors influencing performance of simulated annealing optimization

This paper statistically analyzes a simulated annealing algorithm to determine the relative importance of different parameters on performance. Using ANOVA analysis, the authors identify which parameters have a statistically significant impact on optimization results across test problems. They determine suitable values through further statistical testing.

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