Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successful optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Ofte...
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Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successful optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Often, heuristic optimization algorithms are inspired by nature but imperialist competitive algorithm, inspired by the laws and policies governing human society, was presented in recent decade. Imperialist competitive algorithm was applied in various fields. This algorithm had a very good performance compared to other optimization algorithms. For this reason, this study tries to modify imperialist competitive algorithm for improve the accuracy and performance of the algorithm. Assimilation operator of the imperialist competitive algorithm is modified. The main motivation of this work is to introduce a powerful heuristic optimization algorithm. A comparison between the proposed imperialist competitive algorithm framework and several versions imperialist competitive algorithm on 6 standard numerical benchmarks and four famous optimization algorithm indicate that the proposed algorithm has a good performance on a wide variety of problems.
Power-saving has become a central issue for well-configured SOC platforms. In particular, as a high percentage of the total energy is used by the storage systems, the cost effectiveness of data management is equally a...
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Power-saving has become a central issue for well-configured SOC platforms. In particular, as a high percentage of the total energy is used by the storage systems, the cost effectiveness of data management is equally as important as reliability and availability. To address this issue, we propose the dynamic grid quorum as a method for reducing the power consumption of large-scale distributed storage systems. The basic principle of our approach is to skewthe-workload toward a small number of quorums. This can be realized using the following three techniques. First, our system allows reconfiguration by exchanging nodes without any datamigration, so that high-capacity nodes can be reallocated to busier quorums. Second, for more effective skewing of the workload, we introduce the notion of dual allocation, which makes it possible to consider two distinct allocations in the same grid for write and read quorums. Finally, we present an optimization algorithm to find a pair of a strategy and an allocation of nodes, which minimizes power for a given system setting and its workload. We also demonstrate that the dynamic grid quorum saves, on average, 14-25% energy compared with static configurations, when the intensity of the total workload changes.
Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) ...
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Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) using carbide tools, utilizing a recently developed multi-objective, nature -inspired metaheuristic algorithm known as the Multi-Objective Grasshopper optimization algorithm (MOGOA). This optimization process's primary objectives are minimizing surface roughness and maximizing the material removal rate. By employing the MOGOA algorithm, the study demonstrates its efficacy in successfully optimizing the cutting parameters. This research's findings highlight the MOGOA algorithm's capability to effectively fine-tune cutting parameters during PA6 machining, leading to improved outcomes in terms of surface roughness reduction and enhanced material removal rate.
To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method ...
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To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.
Nowadays, because of the enormous increase in load demand, the electrical distribution system faces problems like poor system efficiency due to high I2R losses and poor voltage profile. Therefore, distribu-tion system...
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Nowadays, because of the enormous increase in load demand, the electrical distribution system faces problems like poor system efficiency due to high I2R losses and poor voltage profile. Therefore, distribu-tion system operators are looking for various alternatives for enhancing system efficiency & voltage pro-file. Distributed generation (DGs) technology has recently been the focus of several researchers due to its enormous technological advantages in mitigating the above problems. In this article, an approach is pre-sented for the optimal integration of dispatchable distributed generations (DDG): PV-BESS (Photovoltaic System-Battery energy storage system), WT-Biomass (Wind Turbine) units in the distribution system in the presence of optimal network reconfiguration. Distribution generations like PV & WT are non-dispatchable in nature due to the intermittency nature of solar radiance and wind speed. The PV unit is supported by BESS, while the WT unit is supported by Biomass to make the PV and WT units dispatch -able. Therefore, the paper's main intent is to determine the best locations & best sizes of PV-BES, Wind -Biomass units in the distribution system in the presence of network reconfiguration considering the time-varying 24-hour load pattern, probabilistic nature of solar irradiance & wind speed. To reduce sys-tem energy loss, voltage deviation index, and annual economic loss, a multi-objective pareto-based veloc-ity butterfly optimization algorithm (MOVBOA) is used. IEEE 33,69 & 118 bus test systems are being used to implement the proposed approach. The MOVBOA algorithm gives better results for solving the prob-lem than the multi-objective Butterfly optimization algorithm (MOBOA) & Non-dominated sorting genetic algorithm (NSGA-II).(c) 2022 Elsevier Ltd. All rights reserved.
The purpose of underwater acoustic sensor networks (UWASNs) is to find varied applications for ocean monitoring and exploration of offshore. In majority of these applications, the network comprises of several sensor n...
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The purpose of underwater acoustic sensor networks (UWASNs) is to find varied applications for ocean monitoring and exploration of offshore. In majority of these applications, the network comprises of several sensor nodes deployed at different depths in water. The sensor nodes which are situated in depth, at the sea bed, are unable to communicate unswervingly with those nodes which are close to the surface level;these nodes necessitate multi-hop communication which is facilitated by suitable routing plan. The working of UWASNs is affected by some constraints like high transmission delay, energy consumption, deployment, long propagation delay and high attenuation. Apart from this, the existence of void region in the route can also affect the overall performance of UWASNs. So, the void region can be avoided by considering the best forwarder node. The selection of the best forwarder node depends on depth variance, depth difference, residual energy, and link quality. Apart from this, an angle is also considered to select the best forwarder node. This paper presents an energy efficient and void region avoidance routing. The concept of grey wolf optimization algorithm is used here to select the best forwarder node. The proposed work increases the network lifetime by avoiding the void region and also balancing the network energy. The proposed work is simulated in the MATLAB platform and compared with weighting depth and forwarding area division depth-based routing and energy and depth variance-based opportunistic void avoidance schemes. This work achieves the packet delivery ratio 96% with varying transmission range up to 1000 m at 180 node size. Along with this, it decreases the end-to-end delay and average number of dead nodes up to 53% and 145, respectively. This work also improves the overall network lifetime and reduces the transmission delay. This work also propagates 55% less copies of data packets. Similar to this, some other performance metrics are also explained
In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing ...
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In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.
In this paper, a new global optimization algorithm is developed, which is named Particle Swarm optimization combined with Particle Generator (PSO-PG). Based on a series of comparable numerical experiments, we show tha...
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In this paper, a new global optimization algorithm is developed, which is named Particle Swarm optimization combined with Particle Generator (PSO-PG). Based on a series of comparable numerical experiments, we show that the calculation accuracy of the new algorithm is greatly improved and optimization efficiency is increased as well, in comparison with those of the standard PSO. It is also found that the optimization results obtained from PSO-PG are almost independent of the coefficients adopted in the algorithm.
Previous underground test methods for coal seam permeability are usually based on radial flow theory, which ignores the impact of coal deformation and permeability dynamic evolution. Thus, these methods have some limi...
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Previous underground test methods for coal seam permeability are usually based on radial flow theory, which ignores the impact of coal deformation and permeability dynamic evolution. Thus, these methods have some limitations in theoretical reliability, result stability and method applicability. Therefore, this paper derives a gas-solid coupling model considering pore sorption strain. Based on this model and a hybrid optimization algorithm, which combines particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithm, a novel method for determining coal seam permeability, namely the GP method, is proposed. The feasibility and reliability of this method were verified by numerical experiments and field tests, respectively. The findings indicate that the proposed PSO + LM algorithm was superior to PSO algorithm and LM algorithm in terms of convergence and computational efficiency. In field application, the test value of the GP method was closer to the true value of coal seam permeability, and its theoretical model can better reflect the change of borehole gas flow, whether compared with the traditional radial flow method from the global perspective or compared with other classical permeability models from the local perspective. Therefore, the GP method has the potential to become an effective test method for coal seam permeability. This study has certain reference significance for the acquisition of mechanical parameters and gas parameters.
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