C Rajendran

In this work we address the problem of transforming a jobshop layout into a flowshop layout with the objective of minimizing the length of the resulting flowline. This problem is a special case of the well-known classical Shortest Common Supersequence (SCS) stringology problem. In view of the problem being NP-hard, an ant-colony algorithm, called PACO-SFR, is proposed. A new scheme of forming an initial supersequence of machines (i.e., flowline) is derived from a permutation of jobs, followed by the reduction in the length of the flowline by using a concatenation of forward reduction and inverse reduction techniques, machine elimination technique and finally an adjacent pair-wise interchange of machines in the flowline. The proposed ant-colony algorithm's performance is relatively evaluated against the best known results from the existing methods by considering many benchmark jobshop scheduling problem instances. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems. © 2011 Springer-Verlag.