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The optimal mapping of tasks to the processors is one of the challenging issues in heterogeneous computing systems. This article presents a task scheduling problem in distributed systems using discrete particle swarm optimization (DPSO) algorithm with various neighborhood topologies. The DPSO is a recent metaheuristic population‐based algorithm. In DPSO, the set of particles in a swarm flies through the N‐dimensional search space by learning from both the personal best position and a neighborhood best position. Each particle inside the swarm belongs to a specific topology for communicating with neighboring particles in the swarm. The neighborhood topology affects the performance of DPSO significantly, because it determines the rate at which information transmits through the swarm. The proposed DPSO algorithm works on dynamic topology that is binary heap tree for communication between the particles in the swarm. The performance of the proposed topology is compared with other topologies such as star, ring, fully connected, binary tree, and Von Neumann. The three well‐known performance measures such as Makespan, mean flow time, and reliability cost are used for the comparison of the proposed topology with other neighborhood topologies. Computational simulation results indicate that the performance of DPSO algorithm has shown significant improvement with binary heap tree topology used for communication among the particles in the swarm.

Iron and zinc deficiency is associated with memory deficits in children. There is a marked improvement in memory after supplementation. Post supplementation IQ scores do not show significant improvement in deficient groups in 6-8 year olds.

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