Using Multi-objective Artificial Fish Swarm Algorithm to Solve the Software Project Scheduling Problem

Abstract

In this paper, a new multi-objective artificial fish swarm algorithm was proposed based on the principles of PAES algorithm and it is used to solve SPSP. The aim of this proposal is to solve the software project scheduling problem with artificial fish swarm algorithm and to overcome some disadvantages that AFSA suffer from. The performance of the proposed algorithm was compared with another multiobjective AFSA based on the use of global information (GAFSA), in terms of speed, quality of produced solutions and complexity of algorithm operations. The results show that the proposed algorithm is faster, easier to implement, require less computations, and had obtained better nondominated solutions than the other algorithm.

Solving software project scheduling problem using grey wolf optimization

“…In order to schedule any project of the software, we need information about: the project tasks, the employees who must TELKOMNIKA Telecommun Comput El Control  Solving software project scheduling problem using grey wolf optimization (Marrwa Abd-AlKareem Alabajee) 1823 work on various tasks and the skills which needed to complete the different tasks of software project. We described this information in detailed and then explained the mathematical models [6], [9]: − Tasks All the major activities that must do to plenary a project are project tasks. These activities may contain the analysis, design of different components, programming, documentation and the entire testing process.…”

“…The amount of maximum dedication for all employees is always assumed to be 1. The aim of SPSP is to reduce both cost and completion time of the project [6], [9], [17]. 𝑡𝑎 6 𝑒𝑚𝑝𝑙1 0.00 0.50 0.00 1.00 0.25 0.00 𝑒𝑚𝑝𝑙2 0.25 0.00 0.75 0.00 0.50 1.00 𝑒𝑚𝑝𝑙3 1.00 0.00 0.25 0.25 0.00 0.00 𝑒𝑚𝑝𝑙4 0.00 0.75 0.00 0.25 0.00 0.50…”

“…In the project, each task's cost must be previously obtained, for calculating later the whole cost of the project(pro cost ), for the task j, the cost (ta j cost ) can be computed utilizing the (5), Then The project's cost is the cost summation of all tasks in the project as illustrated in (6) [6], [9]:…”

“…SPSP is considered as a combinatorial optimization problem (COP) which has several different possible allocations between tasks and employees and every allocation has different time as well as cost. The SPSP has been resolved using incomplete techniques (metaheuristics and heuristics appraoches) which check only possible allocations parts in order to find near optimal solutions in an acceptable time and effort [9].…”

“…Almshhadany and Ibrahim [9] introduced a new algorithm of swarm intelligence (multi-objective artificial fish) based on the Pareto archived evolution strategy (PAES) algorithm principles and it was used to solve the problem of SPSP. At the same years, Crawford et al [6] proposed metaheuristic intelligent water drops for solving the SPSP optimization problems, it obtained a good result.…”