Optimization of Project Scheduling Problem Using Multi-Objective Bat-inspired Algorithm and comparison with other Nature Inspired Algorithms

In the practice of software engineering, project managers often face the problem of software project management. It is related to resource constrained project scheduling problem. In software project scheduling, main resources are considered to be the employees with some skill set and required amount of salary. The main purpose of software project scheduling is to assign tasks of a project to the available employees such that the total cost and duration of the project are minimized, while keeping in check that the constraints of software project scheduling are fulfilled. Software project scheduling (SPSP) has complex combined optimization issues and its search space increases exponentially when number of tasks and employees are increased, this makes software project scheduling problem (SPSP) a NP-Hard problem. The goal of software project scheduling problem is to minimize total cost and duration of project which makes it multi-objective problem. Many algorithms are proposed up till now that claim to give near optimal results for NP-Hard problems, but only few are there that gives feasible set of solutions for software project scheduling problem, but still we want to get more efficient algorithm to get feasible and efficient results. Nowadays, most of the problems are being solved by using nature inspired algorithms because these algorithms provide the behavior of exploration and exploitation. For solving software project scheduling (SPSP) some of these nature inspired algorithms have been used e.g. genetic algorithms, Ant Colony Optimization algorithm (ACO), Firefly etc. Nature inspired algorithms like particle swarm optimization, genetic algorithms and Ant Colony Optimization algorithm provides more promising result than naive and greedy algorithms. However there is always a quest and room for more improvement. The main purpose of this research is to use bat algorithm to get efficient results and solutions for software project scheduling problem. In this work modified bat algorithm is implemented where a different approach of random walk is used. The contributions of this thesis are to: (1) To adapt and apply modified multi-objective bat algorithm for solving software project scheduling (SPSP) efficiently, (2) to adapt and apply other nature inspired algorithms like genetic algorithms for solving software project scheduling (SPSP) and (3) to compare and analyze the results obtained by applied nature inspired algorithms and provide the conclusion