A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning
The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9921223/ |
| _version_ | 1817978199692279808 |
|---|---|
| author | Peng Wang Zhiliang Deng |
| author_facet | Peng Wang Zhiliang Deng |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness. |
| first_indexed | 2024-04-13T22:27:02Z |
| format | Article |
| id | doaj.art-9e4356ae3f494777ba0314ec74441cf2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T22:27:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9e4356ae3f494777ba0314ec74441cf22022-12-22T02:27:03ZengIEEEIEEE Access2169-35362022-01-011011049711051110.1109/ACCESS.2022.32151319921223A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path PlanningPeng Wang0https://orcid.org/0000-0002-6223-9215Zhiliang Deng1School of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing, ChinaThe traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.https://ieeexplore.ieee.org/document/9921223/Seagull optimization algorithmmulti-objective problemqubits encodingpath planning |
| spellingShingle | Peng Wang Zhiliang Deng A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning IEEE Access Seagull optimization algorithm multi-objective problem qubits encoding path planning |
| title | A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning |
| title_full | A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning |
| title_fullStr | A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning |
| title_full_unstemmed | A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning |
| title_short | A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning |
| title_sort | multi objective quantum inspired seagull optimization algorithm based on decomposition for unmanned aerial vehicle path planning |
| topic | Seagull optimization algorithm multi-objective problem qubits encoding path planning |
| url | https://ieeexplore.ieee.org/document/9921223/ |
| work_keys_str_mv | AT pengwang amultiobjectivequantuminspiredseagulloptimizationalgorithmbasedondecompositionforunmannedaerialvehiclepathplanning AT zhiliangdeng amultiobjectivequantuminspiredseagulloptimizationalgorithmbasedondecompositionforunmannedaerialvehiclepathplanning AT pengwang multiobjectivequantuminspiredseagulloptimizationalgorithmbasedondecompositionforunmannedaerialvehiclepathplanning AT zhiliangdeng multiobjectivequantuminspiredseagulloptimizationalgorithmbasedondecompositionforunmannedaerialvehiclepathplanning |