Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Ant Colony Optimization. A Computational Intelligence Technique

Ant colony optimization (ACO) is a novel computational technique inspired by a foraging behavior of ants has been successfully applied for solving real world optimization problems. This behavioral pattern inspires artificial ants for the search of solutions to the various types of optimization problems. ACO is a probabilistic search approach founded on the idea of evolutionary process. In this paper, we present an overview of ant colony optimization and ACO variants up to now. we also summarize various types of applications. Finally we focus on some research efforts directed at receiving a dipper understanding of the ant colony optimization algorithms

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

U radu se daje prijedlog dizajna paradigme algoritma za optimizaciju kolonije mrava primjenom Hyper-Cube sustava za rješenje problema programiranja računarskog projekta (Software Project Scheduling Problem). Taj se NP-hard problem sastoji od davanja zaduženja zaposlenicima u svrhu smanjenja trajanja projekta i njegovih ukupnih troškova. To zaduženje mora zadovoljiti ograničenja problema i pitanje prvenstva među zadacima. Pristup prikazan ovdje koristi Hyper-Cube sustav za uspostavljanje eksplicitno multidimenzionalnog prostora za kontrolu ponašanja mravi. Time nam se omogućava autonomno vođenje istraživanja u cilju pronalaženja ohrabrujućih rješenja.This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions

Hypercube FrameWork for ACO applied to timetabling

We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

Solving the Software Project Scheduling Problem Using Intelligent Water Drops

Within the category of project scheduling problems, there is a specific problem within the software industry referred to as the software project scheduling problem. The problem consists in the correct allocation of employees to the different tasks that make up a software project, bearing in mind time and cost restraints. To achieve this goal, the present work first uses metaheuristic intelligent water drops illustrating; this is a recent stochastic swarm-based method increasingly used for solving optimization problems. Finally, the results and comparisons with experiments performed with other techniques are presented, demonstrating the solidity of the approach presented