Optimal Resource Sharing Amongst Device-to-Device Communication Using Particle Swarm Algorithm

Nethravathi, H. M.; Akhila, S.

doi:10.1007/978-981-15-5258-8_1

Cited by 1 publication

(1 citation statement)

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“…Different from market-based algorithm, PSO, an centralized algorithm, can offer the global optimal solution, and it has been implemented to the field of task allocation, for example, Yu [33] presented an improved particle swarm optimization (IPSO) algorithm to improve the efficiency of resource scheduling, and this IPSO algorithm can overcome the problem of premature; Nethravathis et al [34] proposed a permutation optimization strategy based on PSO algorithm to solve the resource sharing among device-to-device communication problem; Lin et al [35] illustrated a new group method to divide rescue tasks into groups according to their distances, and employed an improved PSO algorithm to assign the grouped tasks to robots, results indicated that the proposed method can increase the success rate of rescue; Singh et al [36] presented a novel PSO algorithm for solving multi-objective flexible job-shop scheduling problem with the goal of finding approximations of the optimal solutions, and its results verified its effectiveness. From the above studies, PSO-based algorithms are effective to gain the optimal solutions, However, they are centralized algorithms, the global information is needed to present the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

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

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

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This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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