On Comparative Study for Two Selective Ant Colony's Optimal Decisions Versus Reconstruction Problem Solving by a Mouse Inside Figure of Eight (8) Maze

  • Hassan M. H. Mustafa Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha
  • Fadhel Ben Tourkia Computer Engineering Department, Al-Baha Private College of Sciences, Al-Baha
Keywords: Artificial neural network modeling, Swarm Intelligence, Tandem Running, House-HuntingAnts, Nature Inspired Computing


This piece of research addresses an interdisciplinary comparative study of two environmental challenging phenomenal issues. Both were associated to two nonhuman creatures characterized by their behavioral intelligent performance concerned with optimal diverse decisions paradigms. More specifically, this paper deals with the comparison study for analogical behavioral learning of social insects (Ants) colony performance, versus performed behavioral learning achievement by an animal considering a mouse inside Figure of eight (8) maze via its brain hippocampus "time cells" neurons.
In more precise details, this paper firstly have demonstrated for Ant Colony System (ACS) the two effective optimal selectivity decisions for : a) The best source location between two food sources that are equidistantly sited away from the original home nest, based upon pheromone trails and following the tandem running regulation & b) The balanced selection performance with the migration speed, in order to minimize exposure to a hostile environment to avoid vulnerability to presumable danger. Secondly, the optimal decisional issue is demonstrated related to mouse's behavioral learning intelligent approach which observed in practice following its active sequential trials aiming to reach the optimal solution for a reconstruction problem during its movement inside figure of eight (8) maze. Finally, after running of realistic Artificial Neural Networks' (ANNS) simulation programs. Interestingly, the obtained results considering models of both of suggested intelligent behavioral learning issues characterized by relevant functional analogy considering changed number of artificial ant agents versus the various number of neurons inside mouse's hippocampus brain area.

Author Biography

Hassan M. H. Mustafa, Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha
Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha


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