Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value. In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations