P-Evolution Aco
P-Evolution: A Novel Approach to Ant Colony Optimization
Akshay Joshi #1, C.G.Nitash #2, Anish Sawkar #3 Prof. Nilesh B Fal Dessai #4 and Prof Vijay Borges #5 # Information Technology Department, Goa College of Engineering, Goa akshayjoshi999@gmail.com1, cgnitash@gmail.com2, anishsawkar@gmail.com3, nfd@gec.ac.in4, vb@gec.ac.in5
Abstract
The Ant Colony Optimization (ACO) technique uses the foraging behaviour of biological ants, which rely on less on memory and depends 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. P-Evolution is a modification to the ACO meta-heuristic which can be applied to a range of problem models, and problem instances. The working of PEvolution is demonstrated on the Travelling Salesman Problem (TSP) problem. This approach could also be used to solve dynamically changing models, which are representative of a number of real life systems.
from the foraging behavior of biological ants. In the real world, ants when searching for food initially starting from nest move randomly in the search space to search for food. As soon as ant finds the food it analyses its quantity and quality and carries some of it back to the nest depositing a chemical pheromone trail on the ground. These pheromone trails are directly proportional to the quality and quantity of the food source and guides other ant to the food source. This indirect communication between the ants through the pheromone trail leads them to find the shortest path between the source and the nest. This property of real ant colony is exploited to artificial ant colony to solve NP-Problems. [2]
Key Words: Travelling Salesperson
Problem (TSP), Ant Colony Optimization (ACO), Stochastic, Meta-heuristic, P-Evolution.
1. Introduction
Ant Colony
Akshay Joshi #1, C.G.Nitash #2, Anish Sawkar #3 Prof. Nilesh B Fal Dessai #4 and Prof Vijay Borges #5 # Information Technology Department, Goa College of Engineering, Goa akshayjoshi999@gmail.com1, cgnitash@gmail.com2, anishsawkar@gmail.com3, nfd@gec.ac.in4, vb@gec.ac.in5
Abstract
The Ant Colony Optimization (ACO) technique uses the foraging behaviour of biological ants, which rely on less on memory and depends 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. P-Evolution is a modification to the ACO meta-heuristic which can be applied to a range of problem models, and problem instances. The working of PEvolution is demonstrated on the Travelling Salesman Problem (TSP) problem. This approach could also be used to solve dynamically changing models, which are representative of a number of real life systems.
from the foraging behavior of biological ants. In the real world, ants when searching for food initially starting from nest move randomly in the search space to search for food. As soon as ant finds the food it analyses its quantity and quality and carries some of it back to the nest depositing a chemical pheromone trail on the ground. These pheromone trails are directly proportional to the quality and quantity of the food source and guides other ant to the food source. This indirect communication between the ants through the pheromone trail leads them to find the shortest path between the source and the nest. This property of real ant colony is exploited to artificial ant colony to solve NP-Problems. [2]
Key Words: Travelling Salesperson
Problem (TSP), Ant Colony Optimization (ACO), Stochastic, Meta-heuristic, P-Evolution.
1. Introduction
Ant Colony
References: [1]Ant Colony Optimization Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi [2]Ant colony optimization theory: A survey Marco Dorigo Christian Blumb 2005 [4]Mathematical Programming Approaches to the Traveling Salesman Problem Adam N. Letchford Andrea Lodi [6]Ant algorithms and stigmergy Marco Dorigo Eric Bonabeaub, Guy Theraulaz [7]Ant colonies for the travelling salesman problem Marco Dorigo a,*, Luca Maria Gambardella [16] ACO-Metaheuristics.org [17]Wikipedia.org Conclusion We have implemented the P-Evolution approach to the TSP problem, and shown improvement in the quality of the parameter