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Article citations


Alberto, C., et al. (1991) Distributed Optimization by Ant Colonies. Elsevier, Amsterdam, 134-142.

has been cited by the following article:

  • TITLE: On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach)

    AUTHORS: Hassan M. H. Mustafa, Fadhel Ben Tourkia, Ramadan Mohamed Ramadan

    KEYWORDS: Artificial Neural Network Modeling, Animal Learning, Bio-Inspired Clever Algorithm, Ant Colony System, Traveling Salesman Problem

    JOURNAL NAME: Open Access Library Journal, Vol.3 No.10, October 31, 2016

    ABSTRACT: This piece of research addresses an interesting comparative analytical study, which considers two concepts of diverse algorithmic computational intelligent paradigms related tightly with Neural and Non-Neural Systems’ modeling. The first computational paradigm was concerned with practically obtained psycho-learning behavioral results after three animals’ neural modeling. These are namely: Pavlov’s, and Thorndike’s experimental work. In addition, the third model is concerned with optimal solution of reconstruction problem reached by a mouse’s movement inside Figure 8 maze. Conversely, second algorithmic intelligent paradigm was originated from observed activities’ results after Non-Neural bio-inspired clever modeling namely Ant Colony System (ACS). These results were obtained after attaining optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance was shown to be similar for both introduced systems. Finally, performances of both intelligent learning paradigms have been shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application was for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.