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To solve a typical NP-hard combinatorial optimization problem-traveling salesman problem, ant colony optimization based on minimum spanning tree(MST-ACO), is presented and the performance is reported. The mechanism of MST-ACO is described from three aspects: adopting dual nearest insertion procedure to initialize the pheromone, integrating reinforcement learning through computing lowbound by 1-minimum...
RM (Reed-Muller) expansions have shown advantages compared with the traditional SOP (Sum-of-Products) forms in the areas of arithmetic logic, reversible logic synthesis and Boolean quantum circuit design. A new algorithm is presented for the conversion between SOP and RM forms of multiple output functions. This procedure is based on the cube set expressions and therefore independent on number of input...
Clustering is one of main technical of data mining, by a kind of non-teacher supervises recognition pattern. Despite its popularity for general clustering, K-means suffers two major shortcomings: the number of clusters K has to be supplied by the user and the search is prone to local minima. This article unifies particle swarm optimization (PSO) algorithm and Bayesian information criterion (BIC),...
The traveling salesman problem (TSP) in operations research is a classical problem in discrete or combinatorial optimization. It is a prominent illustration of a class of problems in computational complexity theory which are classified as NP-hard. Ant colony optimization inspired by co-operative food retrieval have been widely applied unexpectedly successful in the combinatorial optimization. This...
To improve efficiency and quality of case retrieval in case-based reasoning system, a case retrieval model based on the artificial neural network (ANN) and nearest neighbor (NN) algorithm is presented. Firstly, the indexes of cases are created in order to shrink the case-searching range, and the BP neural network is applied to memorize the product cases that are indexed. Secondly, the similar cases,...
In this paper, we introduce an image alignment method by maximizing a Tsallis entopy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. Due to its convexity property, this divergence measure attains its maximum value when the conditional intensity probabilities between the reference image and the transformed target image are degenerate distributions. Experimental...
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