Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.

This study aims to produce a novel optimization algorithm, called the Cuckoo Search Algorithm (CS), for solving the genome sequence assembly problem. Assembly of genome sequence is a technique that attempts to rebuild the target sequence from the collection of fragments. This study is the first application of the CS for DNA sequence assembly problem in the literature. The algorithm is based on the levy flight behaviour and brood parasitic behaviour. The CS algorithm is employed to maximize the overlap score by reconstructing the original DNA sequence. Experimental results show the ability of the CS to find better optimal genome assembly. To check the efficiency of the proposed technique the results of the CS is compared with one of the well known evolutionary algorithms namely, particle swarm optimization (PSO) and its variants.