Search results for "Global optimum"
showing 3 items of 3 documents
Communities of Local Optima as Funnels in Fitness Landscapes
2016
We conduct an analysis of local optima networks extracted from fitness landscapes of the Kauffman NK model under iterated local search. Applying the Markov Cluster Algorithm for community detection to the local optima networks, we find that the landscapes consist of multiple clusters. This result complements recent findings in the literature that landscapes often decompose into multiple funnels, which increases their difficulty for iterated local search. Our results suggest that the number of clusters as well as the size of the cluster in which the global optimum is located are correlated to the search difficulty of landscapes. We conclude that clusters found by community detection in local…
Adaptive memory programming for constrained global optimization
2010
The problem of finding a global optimum of a constrained multimodal function has been the subject of intensive study in recent years. Several effective global optimization algorithms for constrained problems have been developed; among them, the multi-start procedures discussed in Ugray et al. [1] are the most effective. We present some new multi-start methods based on the framework of adaptive memory programming (AMP), which involve memory structures that are superimposed on a local optimizer. Computational comparisons involving widely used gradient-based local solvers, such as Conopt and OQNLP, are performed on a testbed of 41 problems that have been used to calibrate the performance of su…
Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks
2016
The contribution of this study is twofold: First, we show that we can predict the performance of Iterated Local Search (ILS) in different landscapes with the help of Local Optima Networks (LONs) with escape edges. As a predictor, we use the PageRank Centrality of the global optimum. Escape edges can be extracted with lower effort than the edges used in a previous study. Second, we show that the PageRank vector of a LON can be used to predict the solution quality (average fitness) achievable by ILS in different landscapes.