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Sudipta Chowdhury,Mohammad Marufuzzaman,Huseyin Tunc,Linkan Bian,William Bullington 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.3
This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling sales-man problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been devel-oped and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently com-pared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration.
Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications
Ruholla Jafari-Marandi,Mojtaba Khanzadeh,Brian K. Smith,Linkan Bian 한국CDE학회 2017 Journal of computational design and engineering Vol.4 No.4
Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets.