Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng, Nan; Chen, Zhiting; Nguyen, Quang; Gong, Dunwei

doi:10.1007/s40747-020-00252-2

Cited by 29 publications

(11 citation statements)

References 44 publications

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“…Although PSO is relatively simple to apply and has a high convergence rate, it is easy to fall into local optimum and premature convergence. In the past few years, many improved PSO algorithms have been proposed to improve the search capability of the PSO algorithm and reduce the probability of PSO falling into a local optimum [19][20][21][22][40][41][42]. As an important parameter of PSO, the improvement of inertia weight can greatly improve the performance of PSO.…”

Section: Related Workmentioning

confidence: 99%

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Liang

et al. 2021

Complex Intell. Syst.

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Wart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.

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Section: Related Workmentioning

confidence: 99%

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Liang

et al. 2021

Complex Intell. Syst.

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“…The state of the art algorithms tradeoff optimality for reduced algorithmic complexity. Several centralized approaches that use particle swarm optimization [11], [12] and genetic algorithms [13]- [15] have been developed to solve MRTA. However, they require the agents continuously communicate to a central server that solves the planning problem and then sends the instructions back.…”

Section: Introductionmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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This paper presents a deep reinforcement learning framework in which agents learn how to schedule and censor themselves amongst the other agents competing for access to a limited communication medium. In particular, the process learns to schedule the communication between agents to improve the performance of task allocation in environments with constrained communication in terms of limited bandwidth and message collision. The proposed approach, called Communication-Aware Consensus-Based Bundle Algorithm (CA-CBBA), extends the previous CBBA [1] in that the learned communication policy enables efficient utilization of the shared medium by prioritizing agents with messages that are important for the mission. Furthermore, agents in denser parts of the network are censored appropriately to alleviate the message collision and hidden node problems. We evaluate our approach in various task assignment scenarios, and the results show that CA-CBBA outperforms CBBA in terms of convergence time, rate of conflict resolution, and task allocation reward. Moreover, we show that CA-CBBA yields a policy that generalizes beyond the training set to handle larger team sizes. Finally, the results on time-critical problems, such as a search-and-rescue mission, show that CA-CBBA also outperforms the baselines considered (e.g., CBBA, MCDGA [2], and ACBBA [3]) in terms of number of unassigned and conflicted tasks in most of the scenarios.

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“…To validate the quality of the solutions found by the heuristic, we compare their results with the optimal solution of the MILP. Furthermore, we compare the heuristic with two meta-heuristics: the Genetic Algorithm (GA) [13] and the Particle Swarm Optimization (PSO) [14]. It is possible to improve QoS levels by making the most efficient use of available resources.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic for Load Distribution on Data Center Hierarchy: A MEC Approach

Vieira¹,

Souza²,

Silva³

et al. 2022

IEEE Access

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Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their QoS requirements, we propose a MILP (Mixed Integer Linear Programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles. We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, largescale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to AG and PSO solutions.

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Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Cited by 29 publications

References 44 publications

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

A Heuristic for Load Distribution on Data Center Hierarchy: A MEC Approach

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