Water is a critically scarce resource for industrial production and social life. The discharge of untreated wastewater into natural waters can pose a serious risk to human health. Most of urban wastewater treatment pl...
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Water is a critically scarce resource for industrial production and social life. The discharge of untreated wastewater into natural waters can pose a serious risk to human health. Most of urban wastewater treatment plants use biochemical methods, the most common of which is the biological reaction through activated sludge to degrade pollutants. Considering the public environmental protection and socio-economic needs, this paper es-tablishes a wastewater treatment process (WWTP) optimization model considering the trade-off between effluent quality (EQ) and energy consumption (EC), and designs an dynamic optimal control framework based on a novel multi-objective evolutionary algorithm (MOEA) to track and control the key variables in the WWTP. The nu-merical experiments of multi-objective test functions show that the proposed MOEA has good convergence and distributivity. Simulation results based on the BSM1 platform show that the constructed framework can accu-rately adjust the set-points of controllers in time to improve the performance, which has broad application prospects in practical applications.
In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynam...
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In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.
To improve the convergence speed and accuracy of the pity beetle algorithm(PBA), we propose an improved algorithm called one-way pioneer guide PBA (OPGPBA). First of all, given the specificity of the random sampling t...
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To improve the convergence speed and accuracy of the pity beetle algorithm(PBA), we propose an improved algorithm called one-way pioneer guide PBA (OPGPBA). First of all, given the specificity of the random sampling technology(RST), resulting in search holes, we propose a new rRST search mechanism, which integrates the random generation mode of the full-space traversal search capability. According to the algorithm's different requirements for neighborhood search, medium-scale search, large-scale search, and global search, the fixed-size mode factor is adaptively processed to make its size change adaptively with iteration. Finally, we propose a mechanism for the pioneer to guide population reproduction. The evolutionary direction of the population in each dimension is determined according to the change in the pioneer position, which effectively improves the convergence ability of the algorithm. Experiments on the CEC2013 test set show that the OPGPBA algorithm has apparent advantages in convergence speed and solution accuracy compared with the three excellent algorithms so far.
The project explores the load-bearing behavior of a planar 9-bar linkage in different states of the ‘effective 4-bar’ reconfiguration sequences based on a robust automated optimization-driven approach of the metaheu...
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The project explores the load-bearing behavior of a planar 9-bar linkage in different states of the ‘effective 4-bar’ reconfiguration sequences based on a robust automated optimization-driven approach of the metaheuristic algorithm, pity beetle algorithm (PBA). Different intermediate configurations depend on the automated optimization-driven analysis, in order to adjust the system’s joints to the desired values during the motion steps involved from an initial to a target position aiming to eliminate the need of multiple actuators. The 9-bar linkage uses a sequence of one degree-of-freedom motion steps by selectively releasing four joints of the primary members at a time and engaging brakes installed on each individual joint and only one geared electrical motor at the base. In the present study, we consider a principal planar 9-bar linkage with initial and target configurations defined on the basis of a quasi-ellipsoid shape of 5.42 and 4.49 m height respectively and 4.66 m span. The objective is to select the optimum motion sequence, in minimizing the brake torques in the locked joints of the structure. The investigation refers to the load-bearing behavior of the structure in all reconfiguration steps under its self-weight. The numerical studies have been conducted with the software MATLAB and Simulink for a Model Based-Design considering the geometrical, mass and inertia characteristics of the planar system. The obtained results demonstrate that the design of reconfigurable engineering structures can benefit from an automated optimization-driven analysis of their sequence selection, from an initial to a target position, for better performance and efficiency.
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