This paper presents anew algorithm for solving the Travelling Salesmm Problem (TSP). The TSP is one of the most known combinatorial optimisation problems. It is about finding the shortest Homilronian cycle relating N cities. The algorithm is inspired from both genetic aigorithms and quantum compirting fields. It exrends the standard genedic dgorifhms by combining them to some concepts and principles provided from quantum computing field szich as giiantum bit, states superposition and intetfterence. The obfained resulls fram fhe application of the proposed algorithm on some insfumes of TSP are signi5canfly bettw than those provided by standard genetic algorithms.
In this paper, wc propose an algorithm of hybrid particle swarm with differential evolution (DE) opcrntor, termed DEPSO, to solve the problem of multimodal image registration.This algorithm combines the robustness of entropy based measures and the search power of the DEPSO which provides the bell-shaped mutations with consensus on the population diversity, while keeps the particle swarm dynamics. The main idea is to find thc hcst transformation that superimposes two multimodal images by maximizing the mutual information value through the DEPSO. We show that this algorithm, besides its simplicity, provides a robust and effrcient way to rigidly register multimodal images in various situations.Inder Terms-particle swarm optimization, differential evolution, multimodal image registration, mutual information. 1161 W. Zhang, and X. Xie. "DEPSO: hybrid particle swarm with differential evolution operator," Proceedings of IEEE Internarionul Conference on
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