Metaheuristic algorithm is a popular research field. In recent years, a host of optimisers have been presented. Dragonfly algorithm (DA) mimicking the behaviour of a dragonfly performs markedly competitive to some opt...
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Metaheuristic algorithm is a popular research field. In recent years, a host of optimisers have been presented. Dragonfly algorithm (DA) mimicking the behaviour of a dragonfly performs markedly competitive to some optimisation problems. However, the DA sometimes does not perform well when encountering some complicated problems, easily falls into the local optimum, and premature convergence. To overcome the deficiencies of the canonical DA, this paper presents an enhanced version of DA, namely the multi-group dragonfly algorithm (MDA). The proposed MDA with three communication strategies applies effectively the multi-group trick to improve the diversity of the population. To verify the performance of the proposed MDA, it is evaluated by different benchmarks including unimodal functions, multimodal functions, hybrid, and composed functions. The experimental data confirm that the MDA performs better than the DA. Besides, the MDA is also applied in the wireless sensor network deployment problem, the simulation results appear that the MDA can obtain a more ideal sensor node distribution.
In response to the shortcomings of particle swarm optimization (PSO) such as insufficient global search, susceptibility to local optima, and slow convergence speed, this paper proposes an improved adaptive PSO (APSO)....
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ISBN:
(纸本)9798350386783;9798350386776
In response to the shortcomings of particle swarm optimization (PSO) such as insufficient global search, susceptibility to local optima, and slow convergence speed, this paper proposes an improved adaptive PSO (APSO). Firstly, a superior point set is employed for particle population initialization, achieving a more uniform and extensive particle distribution. Secondly, during the particle velocity update phase, an adaptive Levy flight acceleration coefficient is introduced along with adjustments to the social learning factor, emphasizing global search. Finally, in the particle position update phase, an adaptive scaling factor is introduced to intelligently adjust particle position weights, aiding in obtaining superior solutions. The proposed APSO is applied to the wireless sensor network deployment problem, and experimental results demonstrate its outstanding performance in solving optimization problems.
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