Ant Colony Optimization and the Solution Proximity
Maxwell Scale Uwadia Osagie *
Department of Computer Science, Faculty of Science, Ebonyi State University, Abakaliki, Nigeria.
Osatohanmwen Enagbonma
Department of Physical Sciences, Faculty of Science, Benson Idahosa University, P.M.B 1100, GRA, Benin City, Edo State, Nigeria.
Amanda Iriagbonse Inyang
Department of Physical Sciences, Faculty of Science, Benson Idahosa University, P.M.B 1100, GRA, Benin City, Edo State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Nature as designed has its complexity but the challenges posed by it gave room for several methods considered to be best fit for human survival. The role of science and other related fields have so far solved some of the challenges that may have classified humans unfit within the design frame. The survival of human in respect to problem solving varies from time to time and the method employed defined the result from the beginning. Ants and other insects are part of the design nature. Empirical studies on insects have showed complex problems resolved effortlessly with better cooperation and collaboration. This is seen from Ants structural design, building of bridges for navigation purposes, and the path routing for food as well as survival. This paper present an experiment carried out to ascertain the ant colony optimization and the solution proximity vis-à-vis solving the complexity posed by nature and humans. The experiment used two varieties of insect. A is the Ant experiment with ratio 26:8 of surviving rate and isolated rate while B is the Fly experiment with ratio 2:10 of the surviving rate and isolated rate respectively.
Keywords: Swarm intelligence, structure, algorithm, navigation, scientist, human