For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI) : Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An elaborate comparative analysis is carried out to endow these algorithms with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.

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To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes.The security attacks are due to the compromise of the large part of the network which can cause node damage or disturbance in the data flow. At the time of re-keying process, if the re-key from the sink is not securely transmitted to the compromised node, it will lead to denial of service attack. In this paper, we propose a cluster based key management technique for authentication in wireless sensor networks. Initially, clusters are formed in the network and the cluster heads are selected based on the energy cost, coverage and processing capacity. By simulation results, we show that the proposed approach recovers the compromised node in the secured manner.

This paper deals with the makespan minimization for Job Scheduling . Research on optimization techniques of the Job Scheduling Problem (JSP) is one of the most significant and promising areas of an optimization. Instead of the traditional optimization method, this paper presents an investigation into the use of an Ant Colony optimization (ACO) to optimize the JSP. The numerical experiments of ACO were implemented in a small JSP. In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm stimulates the behavior of ants. The main objective of this paper is to minimize the makespan time of a given set of jobs and achieved optimal results are encroached.