Abstract-Hybrid algorithm based on Particle SwarmOptimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.

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Data mining is the collection of different techniques. Clustering information into various cluster is one of the data mining technique. It is a method, in which each cluster must contain more similar data and have much dissimilarity between inter cluster data. Most of traditional clustering algorithms have disadvantages like initial centroid selection, local optima, low convergence rate etc. Clustering with swarm based algorithms is emerging as an alternative to more conventional clustering techniques. In this paper, a new hybrid sequential clustering approach is proposed, which uses PSO -a swarm based technique in sequence with Fuzzy k -means algorithm in data clustering. Experimentation was performed on standard dataset available online. From the result, the proposed approach helps to overcome limitations of both algorithms, improves quality of formed cluster and avoids being trapped in local optima.

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