This paper investigates the relation between the distribution of the weights and the number of local optima in the Number Partitioning Problem (NPP). The number of local optima in the 1-bit flip landscape was found to be strongly and negatively correlated with the coefficient of variation (CV) of the weights. The average local search cost using the 1- bit flip operator was also found to be strongly and negatively correlated with the CV of the weights. A formula based on the CV of the weights for estimating the average number of local optima in the 1-bit flip landscape is proposed in the paper. The paper also shows that the CV of the weights has a potentially useful application in guiding the choice of heuristic search algorithm.
CITATION STYLE
Alyahya, K., & Rowe, J. E. (2014). Local optima and weight distribution in the number partitioning problem. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8672, 862–871. https://doi.org/10.1007/978-3-319-10762-2_85
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