Akshay Joshi, C.G.Nitash
The ACO technique uses the foraging behaviour of biological ants, which rely on less memory and more on collective intelligence. These same principles are used to create artificial ants which iteratively develop partial solutions to the problem and hence obtain optimal solutions. The objective of this paper is to develop a novel approach to the ACO meta-heuristic called P-Evolution. This is a modification to the ACO meta-heuristic which can be applied to a range of problem models, and problem instances. The working of P-Evolution is demonstrated on the TSP problem. This approach could also be used to solve dynamically changing models, which are representative of a number of real life systems.
@article{606bd17b-4d25-45ea-a11d-933df6e525ae,
title={P Evolution A Novel Approach To Ant Colo},
author={Akshay Joshi and C.G.Nitash},
year={2026},
language={en}
}TY - JOUR TI - P Evolution A Novel Approach To Ant Colo AU - Akshay Joshi AU - C.G.Nitash PY - 2026 LA - en ER -
This paper addresses the challenge of assessing the feasibility of wind power plant projects at sites with insufficient or no local historic wind data
Important advances in electrochemical engineering technology over the last three decades have fostered the development of a lternative methods to alle
Increasing volumes of waste printed circuit boards from obsolete electronic equipment posed escalating environmental risks and resource losses due to
The leachability tests for manufacturing scrap TV boards (STVB) have indicated the release of metals beyond the limit levels with potential problems f