An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements
Апстракт
This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark prob...lems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.
Кључне речи:
localization / time difference of arrival / butterfly optimization algorithm / hybrid optimization / diferential evolution / Cramer-Rao lower boundИзвор:
Applied Sciences, 2023, 13, 2Издавач:
- MDPI, Basel, Switzerland
Финансирање / пројекти:
- Развој методологија за повећање радне способности, поузданости и енергетске ефикасности машинских система у енергетици (RS-MESTD-Technological Development (TD or TR)-35029)
- Одрживост и унапређење машинских система у енергетици и транспорту применом форензичког инжењерства, еко и робуст дизајна (RS-MESTD-Technological Development (TD or TR)-35006)
- Напредне технике ефикасног коришћења спектра у бежичним системима (RS-MESTD-Technological Development (TD or TR)-32028)
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Rosić Vitas, Maja AU - Sedak, Miloš AU - Simić, Mirjana AU - Pejović, Predrag V. PY - 2023 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5360 AB - This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms. PB - MDPI, Basel, Switzerland T2 - Applied Sciences T1 - An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements IS - 2 VL - 13 DO - 10.3390/app13020684 ER -
@article{ author = "Rosić Vitas, Maja and Sedak, Miloš and Simić, Mirjana and Pejović, Predrag V.", year = "2023", abstract = "This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.", publisher = "MDPI, Basel, Switzerland", journal = "Applied Sciences", title = "An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements", number = "2", volume = "13", doi = "10.3390/app13020684" }
Rosić Vitas, M., Sedak, M., Simić, M.,& Pejović, P. V.. (2023). An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements. in Applied Sciences MDPI, Basel, Switzerland., 13(2). https://doi.org/10.3390/app13020684
Rosić Vitas M, Sedak M, Simić M, Pejović PV. An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements. in Applied Sciences. 2023;13(2). doi:10.3390/app13020684 .
Rosić Vitas, Maja, Sedak, Miloš, Simić, Mirjana, Pejović, Predrag V., "An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements" in Applied Sciences, 13, no. 2 (2023), https://doi.org/10.3390/app13020684 . .