ACO powered by Local Searches Algorithms for the Solution of TSP Problems

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Addressing Examination Timetabling Problem Using a Partial Exams Approach in Constructive and Improvement

The paper investigates a partial exam assignment approach for solving the examination timetabling problem. Current approaches involve scheduling all of the exams into time slots and rooms (i.e., produce an initial solution) and then continuing by improving the initial solution in a predetermined number of iterations. We propose a modification of this process that schedules partially selected exams into time slots and rooms followed by improving the solution vector of partial exams. The process then continues with the next batch of exams until all exams are scheduled. The partial exam assignment approach utilises partial graph heuristic orderings with a modified great deluge algorithm (PGH-mGD). The PGH-mGD approach is tested on two benchmark datasets, a capacitated examination dataset from the 2nd international timetable competition (ITC2007) and an un-capacitated Toronto examination dataset. Experimental results show that PGH-mGD is able to produce quality solutions that are competitive with those of the previous approaches reported in the scientific literature.

Multi-Hop Leach using the Great Deluge Optimization in Wireless Sensor Optimization

The Wireless Sensor Networks (WSNs) today is gaining plenty of attention due to its wide application range. The reduction of consumption of energy of their sensor nodes has been considered to be a crucial challenge for operating the WSNs in the long run. Low-Energy Adaptive Clustering Hierarchy (LEACH) which is an extremely popular technique which can form several clusters by means of employing a distributed algorithm. The Multi-Hop Low Energy Adaptive Clustering Hierarchy (MH-LEACH) has minimized the consumption of energy at the time of transmission of data among the Cluster Heads (CH) and Base Station (BS). The load is further reduced by multi-hop routing wherein packets are duplicated within the network to improve the efficiency of energy for the WSN. A heuristic method of local search is the Great Deluge Algorithm (GDA) which is employed for solving problems in optimization. In this work, a multi-hop LEACH along with the Great Deluge Algorithm that has been proposed for efficiency in clustering of the WSN.