## 7 Conclusions and Future Work  
Particle swarm optimization (PSO) is widely used for different applications in software engineering domains such as in the next release problem, risk factor prioritization, and software-effort estimation. There are numerous variants of the PSO algorithm proposed to improve its performance. However, PSO still suffers from two primary shortcomings-- premature convergence and easy trap in local optima. This study proposes modified chaotic particles swarm optimization with uniform initialized particles (MUCPSO). This algorithm works well based on the following three schemes: uniform initialization, chaotic inertia mapping, and a stochastic personal learning strategy. Uniform initialization is applied to ensure the diversity of the initialized particle, while chaotic inertia weight plays a role in maintaining the balance between the exploration and exploitation phases, and personal learning strategy helps to enhance the global and local search to avoid trap in local optima. The evaluation of three estimation method benchmarks shows that it significantly outperforms all the competitors, including SPSO, CPSO, GA, and UCPSO, where it reaches the Friedman mean rank of 1.67, with \(p\)-values of the Wilcoxon rank-sum test of less than 0.05 for most of the benchmarks. Detailed investigations prove that all the proposed schemes work well in their designed and mean MUCPSO can effectively control the exploration and exploitation balance. The proposed schemes mean MUCPSO is able to handle most of the various estimation method benchmark functions.  
Several directions for future studies are suggested. First, due to the slightly worse result yielded by MUCPSO compared with CPSO in the COCOMO estimation method benchmark, in future, further studies will be conducted by increasing the generation number. This scenario is important to provide more room to explore MUCPSO and the probability of the best result that can be achieved. Second, the addition of further data and another variants of the estimation method should be employed to enhance the performance of the MUCPSO algorithm.  
Figure 13: Best solution diversity analysis for Agile of (**a**) four algorithms (SPSO, CPSO, UCPSO, MUCPSO); (**b**) all algorithms including GA.  
**Author Contributions:** Conceptualization, A.A.; methodology, A.A.; software, A.A.; validation, A.A., R.F. and A.E.P.; formal analysis, A.A., R.F. and A.E.P.; investigation, A.A., R.F. and A.E.P.; resources, A.A.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, R.F. and A.E.P.; visualization, supervision, R.F. and A.E.P.; project administration, A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in [[https://bit.ly/3](https://bit.ly/3) hvw8e0]([https://bit.ly/3](https://bit.ly/3) hvw8e0). Publicly available datasets were analyzed in this study. This data can be found here: doi:10.17632/2 rfkjh3 cn.1 [53], doi:10.5281/zenodo.268419 [54,55].  
**Conflicts of Interest:** The authors declare no conflict of interest.