Optimization of Association Rule Using Heuristic Approach

Apriori algorithm is used to create all possible association rules among the items in the database, on the behalf of Association Rule Mining and Apriori Algorithm. Here proposed a new algorithm based on the Ant Colony Optimization algorithm to improve the result of association rule mining. Ant Colony Optimization (ACO) is a meta-heuristic approach that inspired by the real behaviour of ant colonies. The association rules create by Apriori algorithm after that find the rules from weakest set based on threshold value that will used the Ant Colony algorithm to reduce the association rules and discover the better quality of rules than apriori. In this research work proposed method focuses on reducing the scans of datasetss by optimization and improving the quality of rules generated for ACO

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets

Heuristics and Metaheuristics Approaches for Facility Layout Problems: A Survey

Facility Layout Problem (FLP) is a NP-hard problem concerned with the arrangement of facilities as to minimize the distance travelled between all pairs of facilities. Many exact and approximate approaches have been proposed with an extensive applicability to deal with this problem. This paper studies the fundamentals of some well-known heuristics and metaheuristics used in solving the FLPs. It is hoped that this paper will trigger researchers for in-depth studies in FLPs looking into more specific interest such as equal or unequal FLPs

Evaluation of using Swarm intelligence to produce facility layout solutions.

The facility layout problem is a combinatorial optimization problem that involves determining the location and shape of various departments within a facility based on inter-department volume and distance measures. An optimal solution to the problem will yield the most efficient layout based on the measures. The application of Particle Swarm Optimization (PSO) was recently proposed as an approach to solving the facility layout problem. With PSO, potential solutions are produced by dividing departments into swarms of Self-Organizing Tiles (SOT). By following a set of simple behavioral rules based on social information gathered from the environment, the tiles cooperate to produce solutions in a very short amount of time. Initial results provided improvements over CRAFT, one of the primary methods currently used for facility layout. The main contribution of this thesis work entails evaluating the use of swarm intelligence to produce optimal facility layouts as well as the use of shape measures to assess the quality of produced layouts. The major achievement of this thesis is the design and implementation of a tool that could produce facility layout solutions using Self- Organizing Tiles (SOT). This thesis advances the swarm paradigm by introducing alternative pathways for achieving contiguity of departments. This thesis utilizes the tool to examine the convergence of SOT on an enumerated optimum for a layout dataset, which requires the exhaustive evaluation of all permutations of a grid layout. The tool was also used to examine the effect of granularity on the ability of SOT to converge on facility layout solutions. A shape metric was utilized as a means of evaluating the quality of produced solutions based on the regularity of the shape of departments, and found that SOT produces fairly regular layouts when granularized to nine tiles per department. Finally, SOT was compared with other algorithms the experimental results revealed that SOT provided minor improvements over currently used methods