# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 6 Conclusions  
The cluster-based multiobjective evolutionary algorithm based on decomposition to find _gbest_ in a bare bones particle swarm optimization clustering framework was developed, tested extensively on 13 UCI datasets and compared with 10 MOC algorithms from a previous study. CM-BBPSO produced competitive results and better results in challenging datasets with highly overlapping clusters that are hard to distinguish, such as CMC, Sonar and Ecoli, when compared with other BBPSOs. The comparison with MOC from our previous work showed that CM-BBPSO produces competitively good results and is ranked highly on datasets with difficult to distinguish clusters, such as CMC and Sonar. Through the analysis of the WSS and BSS values, we observed that the effect of clustering in MOEA balances between compactness and well-separatedness characteristics, whereas M-BBPSO and BBPSO highly prioritize one over the other. This demonstrates that the clustering adds another layer of search control in the selection of candidates in MOEA, meaning that a local search is introduced during the dominance evaluation of new solutions against neighbors in the objective space. This opens up new research directions to explore novel local search, using clustering in the objective space of MOC. Based on a comparative study of the results and methodologies in the reported literature, we found the results of our proposed technique, which uses almost no parameters, to be promising and have identified several areas for future investigation, such as in applying a cluster-based approach to M-Fuzzy, increasing k, and investigation of the choice of objective functions. The proposed technique, though it did not produce the best results, outperformed two of the reported MOC algorithms, VAMOSA and MOAC, on several datasets. We demonstrated that, using simple techniques, such as MOEA/D, BBPSO and K-means, the proposed technique is able to perform competitively. The results from different experimental settings of swarm size and maximum iteration applied on CM-BBPSO showed that those with a smaller swarm size and maximum iteration of 20 and 100, respectively, can achieve good results, which can also be examined visually using the biplots. Furthermore, the convergence plots of CM-BBPSO showed that the algorithm stabilizes as it reaches maximum iterations. With the application of advanced techniques or additional objective functions, such as those found in HT-MOC and MOFC-TMS, novel cluster-based MOEA can be developed to solve complex clustering problems. We hope that the discussion of this work, together with the extensive comparisons against existing MOCs across 13 datasets and 6 metrics, can provide a useful reference and demonstration of the feasibility and effectiveness of CM-BBPSO clustering to new or interested practitioners of BBPSO clustering, MOC and cluster-based MOC algorithms. **Author Contributions:** Conceptualization, D.T.C.L. and Y.S. ; methodology, D.T.C.L. and Y.S. ; validation, D.T.C.L. ; investigation, D.T.C.L. ; resources, D.T.C.L. ; data curation, D.T.C.L. ; writing--original draft preparation, D.T.C.L. ; writing--review and editing, D.T.C.L. and Y.S. ; visualization, D.T.C.L. ; supervision, D.T.C.L. ; project administration, D.T.C.L. ; funding acquisition,D.T.C.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Universiti Brunei Darussalam Grant UBD/RSCH/1.11/FICBF(b)/2019/001, Hosei University under Hosei International Fund Foreign Scholars Fellowship and JSPS KAKENHI Grant Number JP19K12162. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php)]([https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php))]. **Conflicts of Interest:** The authors declare no conflict of interest.