Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes

Volke, Sebastian; Bin, Simon; Zeckzer, Dirk; Middendorf, Martin; Scheuermann, Gerik

doi:10.1007/978-3-642-41888-4_17

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…This work has been used in the analysis and visualization of combinatorial optimization [1,14,27,28]. However, all of these methods require the set of all solutions below a certain cost threshold to be available.…”

Section: Related Workmentioning

confidence: 99%

A Visual Method for Analysis and Comparison of Search Landscapes

Volke

Zeckzer

Scheuermann

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Combinatorial optimization problems and corresponding (meta-)heuristics have received much attention in the literature. Especially, the structural or topological analysis of search landscapes is important for evaluating the applicability and the performance of search operators for a given problem. However, this analysis is often tedious and usually the focus is on one specific problem and only a few operators. We present a visual analysis method that can be applied to a wide variety of problems and search operators. The method is based on steepest descent walks and shortest distances in the search landscape. The visualization shows the search landscape as seen by the search algorithm. It supports the topological analysis as well as the comparison of search landscapes. We showcase the method by applying it to two different search operators on the TSP, the QAP, and the SMTTP. Our results show how differences between search operators manifest in the search landscapes and how conclusions about the suitability of the search operator for different optimizations can be drawn.

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Section: Related Workmentioning

confidence: 99%

A Visual Method for Analysis and Comparison of Search Landscapes

Volke

Zeckzer

Scheuermann

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Self Cite

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“…FL uses the search space's features, and its orthogonal projection materialized by the cost function; with the use of fitness function (cost function), FL measures the number of peaks, the rate of neutral areas, the rate of basins of attraction, etc. (Volke, Bin, Zeckzer, Middendorf, & Scheuermann, 2014). E.g., for a smooth landscape with a single optimum will be relatively easy to solve for many algorithms, while a very rugged landscape, with many local optima, may be more difficult to solve (Horn & Goldberg, 1995;Kauffman & Johnsen, 1991).…”

Section: Related Workmentioning

confidence: 99%

Models to classify the difficulty of genetic algorithms to solve continuous optimization problems

et al. 2023

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