In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility shows a significant part in the enhancement ...
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In recent decades, Sensor nodes (SNs) are used in numerous uses of heterogeneous wireless sensor networks (HWSNs) to obtain a variety of sensing data sources. Sink mobility shows a significant part in the enhancement of sensor system execution, energy utilization, and lifetime. To manage sink mobility, rendezvous points (RPs) are introduced where some SNs are chosen as RPs, and the non-RP nodes convey the information to the cluster heads (CHs). The CHs then forward their information to the nearby RPs. To determine the set of RPs and travelling path of mobile sinks (MSs) that visits these RPs is quite challenging. This work presents an energy-efficient SOSS based routing method that depends on RPs and multiple MSs in HWSNs. At first, all the heterogeneous nodes are distributed into the number of clusters using mean shift clustering (MSC). Then, the Bald eagle search (BES) algorithm is used for an optimal selection of CHs whereas multiple MS is employed for effective data gathering. The use of multiple MSs can enhance the data collection efficiency and decreases the energy utilization for HWSNs. Finally, the hybrid seagull optimization and salp swarm (SOSS) algorithm is used to find the RPs and travelling routes of MS. The entire simulation work of the heterogeneous network is simulated in the NS2 platform. The simulation outcomes display that the suggested method provides superior performance in HWSN than other current routing protocols.
In this paper, we aim at the problem of rapid loss of population diversity encountered in the application of PSO algorithm. A feedback strategy is proposed to maintain population diversity. In order to balance detecti...
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In this paper, we aim at the problem of rapid loss of population diversity encountered in the application of PSO algorithm. A feedback strategy is proposed to maintain population diversity. In order to balance detection and development capabilities, the adjustment of inertia weights is also studied, and a new adaptive particle swarm optimization algorithm is proposed. Through the iterative comparison test of APSO algorithm and LDW algorithm, it is confirmed that APSO has higher robustness and accuracy.
Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative technique...
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Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.
This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters ...
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This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.
This paper studies the effect of data homogeneity on multi-agent stochastic optimization. We consider the decentralized stochastic gradient (DSGD) algorithm and perform a refined convergence analysis. Our analysis is ...
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Aiming at the problem of optimal power flow in photovoltaic (PV) Inverters, this paper proposes a real-time algorithm for optimal power flow (OPF) in distributed PV inverters with sophisticated communication technolog...
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Aiming at the problem of optimal power flow in photovoltaic (PV) Inverters, this paper proposes a real-time algorithm for optimal power flow (OPF) in distributed PV inverters with sophisticated communication technologies and measurement. A higher accuracy and computation speed are obtained by proposing a linear, time-varying optimization method, and the objective function and constraints were linearized using the Taylor expansion under the premise of small variable changes during the short control period. By comparing with the constant linearization method, the proposed time-varying linear approximation method is more accurate and efficient. The problems faced by the centralized calculation is overcome by proposing a distributed control method which utilizes the alternating direction method of multipliers. The proposed distributed control method enables the distributed PV inverters to optimize their own power setpoints with only a little information required from neighbor nodes. Case study results demonstrate that the proposed algorithm makes the distributed PV Inverters more efficient and economical than the other methods.
Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. Whil...
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Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.
With the continuous improvement and maturity of keyword extraction technology, its application scope continues to expand and has now penetrated into multiple fields. This study innovatively introduces the concept of w...
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In structural design of steel frames, in order to achieve proper safety, the effect of uncertainties in the design and loading parameters of the structure must be considered. This approach is obtained by defining a re...
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In structural design of steel frames, in order to achieve proper safety, the effect of uncertainties in the design and loading parameters of the structure must be considered. This approach is obtained by defining a reliability index. In this study, the Modified Dolphin Monitoring (MDM) operator was used to evaluate the reliability index of three well-known steel frame structures based on the Hasofer-Lind method. The reliability index was evaluated using the EVPS and VPS algorithms and with considering the MDM operator on them. The constraint of the last story drift is considered as limit state function. The random variables consist of external loads, modulus of elasticity, moment of inertia and cross-sectional areas. According to the number of evaluations of the limit state function, the results show the efficiency of this method in comparison to the Monte Carlo simulation method. Also, the values of the most probable point (MPP) are examined.
Surrogate-based optimisation is a notable approach for problems, where evaluating the objective function is expensive. These methods construct a model of the objective function to guide the search for optimal solution...
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