An improved adaptive hybrid firefly differential evolution algorithm for passive target localization
Само за регистроване кориснике
2021
Чланак у часопису (Објављена верзија)
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This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employ...ed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer-Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.
Кључне речи:
Time of arrival / Localization / Hybrid optimization / Firefly algorithm center dot / Differential evolution / Cramer-Rao lower boundИзвор:
Soft Computing, 2021, 25, 7, 5559-5585Издавач:
- Springer, New York
Финансирање / пројекти:
- Напредне технике ефикасног коришћења спектра у бежичним системима (RS-MESTD-Technological Development (TD or TR)-32028)
DOI: 10.1007/s00500-020-05554-8
ISSN: 1432-7643
WoS: 000607487500003
Scopus: 2-s2.0-85099355440
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Rosić Vitas, Maja AU - Simić, Mirjana AU - Pejović, Predrag V. PY - 2021 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3614 AB - This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer-Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. PB - Springer, New York T2 - Soft Computing T1 - An improved adaptive hybrid firefly differential evolution algorithm for passive target localization EP - 5585 IS - 7 SP - 5559 VL - 25 DO - 10.1007/s00500-020-05554-8 ER -
@article{ author = "Rosić Vitas, Maja and Simić, Mirjana and Pejović, Predrag V.", year = "2021", abstract = "This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramer-Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms.", publisher = "Springer, New York", journal = "Soft Computing", title = "An improved adaptive hybrid firefly differential evolution algorithm for passive target localization", pages = "5585-5559", number = "7", volume = "25", doi = "10.1007/s00500-020-05554-8" }
Rosić Vitas, M., Simić, M.,& Pejović, P. V.. (2021). An improved adaptive hybrid firefly differential evolution algorithm for passive target localization. in Soft Computing Springer, New York., 25(7), 5559-5585. https://doi.org/10.1007/s00500-020-05554-8
Rosić Vitas M, Simić M, Pejović PV. An improved adaptive hybrid firefly differential evolution algorithm for passive target localization. in Soft Computing. 2021;25(7):5559-5585. doi:10.1007/s00500-020-05554-8 .
Rosić Vitas, Maja, Simić, Mirjana, Pejović, Predrag V., "An improved adaptive hybrid firefly differential evolution algorithm for passive target localization" in Soft Computing, 25, no. 7 (2021):5559-5585, https://doi.org/10.1007/s00500-020-05554-8 . .