## 11 Conclusions  
This paper investigates the TDOA-based localization of a passive target in the presence of noise, which is in general a complex and multimodal problem. To solve this problem, the evolutionary algorithms DE and BOA are hybridized, resulting in the ICDEBOA algorithm. This paper aims to investigate the proposed ICDEBOA algorithm's performance and capabilities on the considered passive localization problem, with a focus on the high measurement noise case, where conventional algorithms perform poorly. In the ICDEBOA algorithm, a composite roulette wheel is introduced to the DE algorithm and employed in a new mutation operator to choose the appropriate mutation scheme for current optimization state. Using BOA expressions for particle movement to generate a mutant vector in the DE algorithm yields a hybrid algorithm that takes advantage of the BOA algorithm's strong local search capability. In addition, an adaptive method incorporating chaos theory is used to update the BOA algorithm's sensory fragrance, and adaptive parameter adjudication strategies from the SHADE algorithm are implemented in the DE algorithm. In order to perform the comparison of the localization performance with several well-known algorithms, the CRLB is derived for the considered problem of localization. In addition, to evaluate the optimization performance of the ICDEBOA algorithm and compare to state-of-the-art methods, a set of CEC2014 test functions are employed and statistical comparison is performed.  
In the results of statistical comparison performed on CEC2014 test problems we observe that the algorithm proposed in this paper shows improved optimization performance. Regarding the localization accuracy, the proposed ICDEBOA algorithm successfully attains the CRLB and outperforms the CWLS, BOA, and SHADE algorithms in that aspect. In the case of a variation of the network topology, we observe that the ICDEBOA algorithm always achieves CRLB and is not affected with the network topology change.  
The focus of upcoming research will be on energy-efficient network structure and performance evaluation in a non-line-of-sight environment. Analyzing the sensitivity to parameter changes can help the optimization process be further improved.  
Figure 8: The change in RMSE performance of the considered algorithms with the increase in the participating transmitters for measurement noise variance \(\sigma^{2}=1\) m\({}^{2}\).  
**Author Contributions:** Conceptualization, M.R. and M.S. ([PERSON]); Methodology and Validation, M.R.; Formal Analysis, Software, Investigation, Data Curation, M.R. and M.S. ([PERSON]); Resources, M.S. ([PERSON]) and P.P.; Writing--Original Draft Preparation, M.R. and M.S. ([PERSON]); Writing--Review and Editing, M.S. ([PERSON]) and P.P.; Visualization, M.R.; Supervision, M.S. ([PERSON]) and P.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research of [PERSON] was supported by the Serbian Ministry of Education and Science under Grant TR35029. The research of [PERSON] was supported by the Serbian Ministry of Education and Science under Grant No. TR35006. The work of [PERSON] was supported in part by Serbian Ministry of Education and Science under Grant TR32028.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.