Fast micro-differential evolution for topological active net optimization

Li, Yuan-Long and Zhan, Zhi-Hui and Gong, Yue-Jiao and Zhang, Jun and Li, Yun and Li, Qing (2016) Fast micro-differential evolution for topological active net optimization. IEEE Transactions on Cybernetics, 46 (6). pp. 1411-1423. ISSN 2168-2275

[img]
Preview
Text (Li-etal-IEEETC2015-Fast-micro-differential-evolution-for-topological-active)
Li_etal_IEEETC2015_Fast_micro_differential_evolution_for_topological_active.pdf
Accepted Author Manuscript

Download (3MB)| Preview

    Abstract

    This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.