A Particle Swarm Optimization Algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

Chen, Zikun; Liu, Hu; Tian, Yajie; Wang, Rui; Xiong, Peisen; Wu, Guoqing

doi:10.1109/access.2020.2990927

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…The samples detected in the simulation are marked in blue, and the POS was calculated using the ratio of the number of detected samples to the total number of samples. For comparison, search areas were also determined by the PSO [20], [21] and MBR [18] [19] methods, which are the most commonly used planning methods. The simulation results are shown in Fig.…”

Section: ) Simulation Results and Discussionmentioning

confidence: 99%

“…Content may change prior to final publication. while the POS of the search areas planned by the TIP, MBR [18] [19], and PSO [20], [21] methods were compared.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

“…Zikun Chen et al parameterized the search area and summarized the optimization objectives and constraint conditions of the search area planning problem. Then, the PSO method was adopted to obtain the global optimal solution by continuously exploring the parameter definition domain [21].…”

Section: Introductionmentioning

confidence: 99%

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A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

et al. 2020

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Helicopters are widely used in maritime search and rescue (SAR) missions. To improve the probability of success (POS) of SAR missions, search areas should be carefully planned. However, the search area is usually determined based on the survivors' probable locations at a given moment by existing planning methods, while the effects of the relative motion between the helicopter and the search objects are ignored, possibly leading to a significant decrease in the POS. To minimize the impact of search object motion, a time domain-based iterative planning (TIP) method is proposed in this paper to obtain the optimal search areas. The survivors' probable locations and mean drift direction are updated iteratively, while the probability map is developed by taking survivors' mean drift direction as a reference. Then, the optimal search area is determined by an iterative search method starting from the cell with the highest probability of containment. To evaluate the effectiveness of a search plan, an agent-based simulation environment of a maritime search mission is constructed based on the AnyLogic simulation platform. Taking a capsizing case as an example, the simulation results show that the novel TIP method minimizes the impact of search object motion on the search effectiveness and obtains higher POS values than those obtained by other methods.

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Section: ) Simulation Results and Discussionmentioning

confidence: 99%

“…Content may change prior to final publication. while the POS of the search areas planned by the TIP, MBR [18] [19], and PSO [20], [21] methods were compared.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

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A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

et al. 2020

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“…Another important problem is that although only common single aircraft operation is considered in this paper, the CEB-mCBR algorithm can also be extended to multiaircraft cooperative operation according to the basic principle of algorithm and the data format of historical cases. Meanwhile, historical information reuse and case-based reasoning theory can also be applied to mission planning in helicopter maritime search and rescue (MSAR) field [38].…”

Section: Discussionmentioning

confidence: 99%

A Multi-CBR Algorithm Based on Comprehensive Evaluation for Operation Planning of Helicopter

et al. 2020

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Case-based reasoning (CBR) is an effective reasoning technology. The core idea of CBR is using past experience and expert knowledge to solve new problems. Operation planning of helicopter is mainly relied on the experiences of the decision-maker, so CBR is an effective reasoning technology which can be used in the operation planning. Two key issues, evaluation method and case adaptation, are widely focused in CBR method. However, the existing CBR is not suitable for non-numeric optimization problem such as operation planning, because it belongs to small sample size problem. To solve this problem, the comprehensive evaluation based multi-CBR (CEB-mCBR) method is proposed in this paper, which can be used for operation planning of helicopter. According to the comprehensive evaluation theory, similar historical case set of the target case is established, and the historical operation plans are ranked. On this basis, the optimum initial plan for the target case is generated by the case screening CBR model. Then the solution element screening CBR model is further put forward to solve the issue of case adaptation. The CEB-mCBR method comprises multi-CBR (case screening CBR and solution element screening CBR) and combination evaluation method. It can integrate expert experience and human thinking better, especially when solution space is limited. Finally, through a case study analysis, the multi-CBR model can generate a feasible solution for the target case, and the solution elements can be adjusted according to the situation in the actual operation, which can improve the efficiency of operation planning while keeping the safety, economy and operating efficiency.

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“…This kind of randomness can increase the probability of finding a better position but can also affect the convergence and the optimality of the solution. Many scholars have taken extensive efforts on the PSO method to balance International Journal of Aerospace Engineering the searching capacity and the convergence accuracy, which mainly include evolution formula improvement, parameter selection, variation strategy, and machine learning [30][31][32][33].…”

Section: 2mentioning

confidence: 99%

Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

Lin

Zhang

Hong-yu

et al. 2021

International Journal of Aerospace Engineering

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This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.

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A Particle Swarm Optimization Algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

Cited by 16 publications

References 26 publications

A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

A Multi-CBR Algorithm Based on Comprehensive Evaluation for Operation Planning of Helicopter

Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

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