A discrete fruit fly optimisation algorithm based on a differential flight strategy (DFFO_DF) is proposed for solving the distributed permutation flowshop scheduling problem with sequence-dependent setup times. In the...
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A discrete fruit fly optimisation algorithm based on a differential flight strategy (DFFO_DF) is proposed for solving the distributed permutation flowshop scheduling problem with sequence-dependent setup times. In the olfactory exploration stage, four types of neighbourhood perturbation operators are designed. An olfactory exploration mechanism is proposed to guide fruit flies during the exploration. In the visual flight stage, to avoid the algorithm from falling into a local optimum, we abandon the mode of all fruit flies flying towards the best individual and propose a differential flight strategy for the fruit flies to make better use of the information of different individuals. A local search method based on critical factories and job blocks helps fruit flies to improve their search capabilities. In addition, the lower bound property is applied to some operators contained in the DFFO_DF to reduce the search space. The proposed algorithm is evaluated using a detailed experimental design to determine the appropriate values of the key parameters. Finally, the proposed DFFO_DF is compared with several state-of-the-art algorithms based on different test instances. The experimental results prove that the DFFO_DF is an effective metaheuristic algorithm. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
The widely acceptable problem in wireless sensor networks (WSNs) is to develop a practical scheme for data aggregation in the massive range of sensor nodes that are randomly distributed over a network region. The esse...
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The widely acceptable problem in wireless sensor networks (WSNs) is to develop a practical scheme for data aggregation in the massive range of sensor nodes that are randomly distributed over a network region. The essential operation of cluster heads (CHs) in such a network is to transmit the aggregated data to the sink node through multi-hop communication, thus the energy to be used in a better way during the period of aggregation and transmission. Therefore, this study presents a scheme based on grid clustering and fuzzy reinforcement-learning to maximise network lifetime as well as energy-efficient data aggregation for distributed WSN. Initially, grid clustering is employed for cluster formation and CH selection. Further, a fuzzy rule system-based reinforcement learning algorithm is used to select the data aggregator node based on the parameters, such as distance, neighbourhood overlap, and algebraic connectivity. Finally, the dynamic relocation of the mobile sink is performed within a grid-based clustered network region using a fruit fly optimisation algorithm. The experimental outcomes revealed that the proposed data aggregation scheme provides superior performance in terms of energy consumption and network lifetime compared to earlier systems.
Different from the classical job shop scheduling, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) should deal with job sequence, machine assignment and worker assignment all together. In t...
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Different from the classical job shop scheduling, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) should deal with job sequence, machine assignment and worker assignment all together. In this paper, a knowledge-guided fruit fly optimisation algorithm (KGFOA) with a new encoding scheme is proposed to solve the DRCFJSP with makespan minimisation criterion. In the KGFOA, two types of permutation-based search operators are used to perform the smell-based search for job sequence and resource (machine and worker) assignment, respectively. To enhance the search capability, a knowledge-guided search stage is incorporated into the KGFOA with two new search operators particularly designed for adjusting the operation sequence and the resource assignment, respectively. Due to the combination of the knowledge-guided search and the smell-based search, global exploration and local exploitation can be balanced. Besides, the effect of parameter setting of the KGFOA is investigated and numerical tests are carried out using two sets of instances. The comparative results show that the KGFOA is more effective than the existing algorithms in solving the DRCFJSP.
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