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Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic

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@article{IJASEIT11504,
   author = {Shaymah Akram Yasear and Ku Ruhana Ku-Mahamud},
   title = {Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2311--2319},
   keywords = {swarm intelligence; metaheuristic; population-based; optimization algorithm.},
   abstract = {

In this paper, a new population update strategy is proposed to overcome the limitations of the non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm. In the NDSHHMO algorithm, the population of hawks is updated based on the average positions of the first three best solutions in the search space. This update strategy leads to the algorithm falling into local optima due to population diversity loss, which causes poor convergence toward the true Pareto front. The proposed population update strategy is inspired by the flush-and-ambush (FA) tactic employed by the Harris’s hawks in nature. The proposed algorithm is called non-dominated sorting Harris’s hawks’ multi-objective optimizer based on the flush-and-ambush tactic (FA-NDSHHMO). The population update strategy in the FA-NDSHHMO includes two main stages, namely, updating the position of hawks using proposed flush-and-ambush movement strategy and selecting the best hawks by using a non-dominated sorting approach to be used in the next generation. The proposed population update strategy aims to improve the search ability of the algorithm, in terms of the diversity of a non-dominated solution and convergence toward the Pareto front. To evaluate the performance of the FA-NDSHHMO algorithm, a set of 10 multi-objective optimization problems has been used. The obtained results show that the new population update strategy has improved the search ability of the FA-NDSHHMO. Furthermore, the results show superiority of the FA-NDSHHMO algorithm compared to the NDSHHMO, multi-objective grasshopper and grey wolf optimization algorithms.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11504},    doi = {10.18517/ijaseit.10.6.11504} }

EndNote

%A Yasear, Shaymah Akram
%A Ku-Mahamud, Ku Ruhana
%D 2020
%T Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic
%B 2020
%9 swarm intelligence; metaheuristic; population-based; optimization algorithm.
%! Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic
%K swarm intelligence; metaheuristic; population-based; optimization algorithm.
%X 

In this paper, a new population update strategy is proposed to overcome the limitations of the non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm. In the NDSHHMO algorithm, the population of hawks is updated based on the average positions of the first three best solutions in the search space. This update strategy leads to the algorithm falling into local optima due to population diversity loss, which causes poor convergence toward the true Pareto front. The proposed population update strategy is inspired by the flush-and-ambush (FA) tactic employed by the Harris’s hawks in nature. The proposed algorithm is called non-dominated sorting Harris’s hawks’ multi-objective optimizer based on the flush-and-ambush tactic (FA-NDSHHMO). The population update strategy in the FA-NDSHHMO includes two main stages, namely, updating the position of hawks using proposed flush-and-ambush movement strategy and selecting the best hawks by using a non-dominated sorting approach to be used in the next generation. The proposed population update strategy aims to improve the search ability of the algorithm, in terms of the diversity of a non-dominated solution and convergence toward the Pareto front. To evaluate the performance of the FA-NDSHHMO algorithm, a set of 10 multi-objective optimization problems has been used. The obtained results show that the new population update strategy has improved the search ability of the FA-NDSHHMO. Furthermore, the results show superiority of the FA-NDSHHMO algorithm compared to the NDSHHMO, multi-objective grasshopper and grey wolf optimization algorithms.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11504 %R doi:10.18517/ijaseit.10.6.11504 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 6 %@ 2088-5334

IEEE

Shaymah Akram Yasear and Ku Ruhana Ku-Mahamud,"Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2311-2319, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.11504.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Yasear, Shaymah Akram
AU  - Ku-Mahamud, Ku Ruhana
PY  - 2020
TI  - Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2311
EP  - 2319
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - swarm intelligence; metaheuristic; population-based; optimization algorithm.
N2  - 

In this paper, a new population update strategy is proposed to overcome the limitations of the non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm. In the NDSHHMO algorithm, the population of hawks is updated based on the average positions of the first three best solutions in the search space. This update strategy leads to the algorithm falling into local optima due to population diversity loss, which causes poor convergence toward the true Pareto front. The proposed population update strategy is inspired by the flush-and-ambush (FA) tactic employed by the Harris’s hawks in nature. The proposed algorithm is called non-dominated sorting Harris’s hawks’ multi-objective optimizer based on the flush-and-ambush tactic (FA-NDSHHMO). The population update strategy in the FA-NDSHHMO includes two main stages, namely, updating the position of hawks using proposed flush-and-ambush movement strategy and selecting the best hawks by using a non-dominated sorting approach to be used in the next generation. The proposed population update strategy aims to improve the search ability of the algorithm, in terms of the diversity of a non-dominated solution and convergence toward the Pareto front. To evaluate the performance of the FA-NDSHHMO algorithm, a set of 10 multi-objective optimization problems has been used. The obtained results show that the new population update strategy has improved the search ability of the FA-NDSHHMO. Furthermore, the results show superiority of the FA-NDSHHMO algorithm compared to the NDSHHMO, multi-objective grasshopper and grey wolf optimization algorithms.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11504 DO - 10.18517/ijaseit.10.6.11504

RefWorks

RT Journal Article
ID 11504
A1 Yasear, Shaymah Akram
A1 Ku-Mahamud, Ku Ruhana
T1 Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2311
OP 2319
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 swarm intelligence; metaheuristic; population-based; optimization algorithm.
AB 

In this paper, a new population update strategy is proposed to overcome the limitations of the non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm. In the NDSHHMO algorithm, the population of hawks is updated based on the average positions of the first three best solutions in the search space. This update strategy leads to the algorithm falling into local optima due to population diversity loss, which causes poor convergence toward the true Pareto front. The proposed population update strategy is inspired by the flush-and-ambush (FA) tactic employed by the Harris’s hawks in nature. The proposed algorithm is called non-dominated sorting Harris’s hawks’ multi-objective optimizer based on the flush-and-ambush tactic (FA-NDSHHMO). The population update strategy in the FA-NDSHHMO includes two main stages, namely, updating the position of hawks using proposed flush-and-ambush movement strategy and selecting the best hawks by using a non-dominated sorting approach to be used in the next generation. The proposed population update strategy aims to improve the search ability of the algorithm, in terms of the diversity of a non-dominated solution and convergence toward the Pareto front. To evaluate the performance of the FA-NDSHHMO algorithm, a set of 10 multi-objective optimization problems has been used. The obtained results show that the new population update strategy has improved the search ability of the FA-NDSHHMO. Furthermore, the results show superiority of the FA-NDSHHMO algorithm compared to the NDSHHMO, multi-objective grasshopper and grey wolf optimization algorithms.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11504 DO - 10.18517/ijaseit.10.6.11504