Real-time workload execution resource provisioning with SLA prerequisite in multi-cloud platform is considered to a difficult job. Data intensive workload is composed direct acyclic graph (DAG);thus, there exist high ...
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Real-time workload execution resource provisioning with SLA prerequisite in multi-cloud platform is considered to a difficult job. Data intensive workload is composed direct acyclic graph (DAG);thus, there exist high dependency among different subtask with varying quality of service (QoS) prerequisite. The existing workload scheduling is designed using multi-objective parameter such as minimizing time and cost;however, reducing delay and energy overhead is not considered. This paper presents Service level agreement-based workload scheduling (SLA-WS) technique for execution of real-time workload on multi-cloud platform. The SLA-WS emphasizes multi-objective parameter such as processing efficiency with energy optimization and task offloading benefits using soft-computing based dragonfly algorithm (DA). The SLA-WS model reduces processing time and energy consumption for execution of different workload in comparison with existing WS-framework leveraging multi-cloud platform.
In this article, A multi-Leaders Guided Harris Hawks optimizer using Epsilon-Dominance relation is developed for solving multi-objective optimization problems. For this reason, the standard HHO algorithm is equipped w...
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In this article, A multi-Leaders Guided Harris Hawks optimizer using Epsilon-Dominance relation is developed for solving multi-objective optimization problems. For this reason, the standard HHO algorithm is equipped with a fixed-size external archive to ensure the elitism concept. On the other hand, both crowding distance computation and epsilon dominance relation are adopted when updating the archive in the hope of improving the diversity of solutions. Moreover, an efficient leader selection procedure is proposed to guarantee convergence towards less-crowded Pareto regions. Our algorithm's performance is validated on 18 test functions in all, 5 with two objectives and 13 with three objectives, and it is compared with four well-regarded algorithms, namely: multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), multi-objective Grey Wolf Optimizer (MOGWO), multi-objective Particle Swarm optimization (MOPSO), and multi-objective Salp Swarm Algorithm (MSSA). Also, it is applied to solve four engineering real-world problems, namely: Four bar truss, Speed reducer, Disk brake design, and Welded beam design problems. Inverted Generational Distance (IGD) metric and Hypervolume (HV) metric were used to quantify the behaviors of multi-objective algorithms. The obtained results show the performance of the proposed algorithm in terms of convergence and diversity for the benchmark functions and the engineering real-world problems.
In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve th...
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In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve the cyber-security of CPPSs. However, most FDIA models are often constructed separately on the effect of attackers or the impact of attacks. Accordingly, this brief proposes a multi-objective stealthy FDIA scheme in AC grid model. The attack model is described as a multi-objective optimization problem, where minimization of contaminated measurements and maximization of the attack impact are considered as two objectives while remaining stealthy. To deal with the established attack model, a non-dominated sorting genetic algorithm II (NSGAII) is introduced as the solver. To improve the efficiency of generating attack vector, a new representation mechanism is proposed to describe locations and values of injected states. Additionally, during the evolutionary process, we propose a mutation operation to balance the sparsity and impact of FDIA. Simulation results on the IEEE 14-bus, 30-bus, and 118-bus systems demonstrate the feasibility of the NSGAII-based FDIA model.
Fluidized beds are widely used in various industrial applications that require efficient mixing, heat and mass transfer between gas and solid particles. This paper explores the relationship between input parameters of...
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ISBN:
(纸本)9798400701207
Fluidized beds are widely used in various industrial applications that require efficient mixing, heat and mass transfer between gas and solid particles. This paper explores the relationship between input parameters of inlet gas velocity into the fluidized bed, by varying the amplitude and the frequency of the velocity, and the objective outputs, such as the total number of bubbles per unit time and the average bubble size in a fluidized bed, using CFD-DEM simulations. The goal of the paper is to define a multi-objective optimization problem and provide an insight into the landscape in the search space. Since the CFD-DEM simulation of a fluidized bed requires a significant computational effort, this landscape analysis is aimed to give an overview and a ground truth for further research in this area.
In this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer;(ii) it is assu...
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In this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer;(ii) it is assumed a single CP for the entire building;(iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a multi-objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.
Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH...
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Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH) capability. A Full-duplex Ambient Backscatter Communication (FAmBC) network with a Full-duplex Access Point (AP), a dedicated Legacy User (LU), and several Backscatter Devices (BDs) is considered in this study. The AP with two antennas transfers downlink Orthogonal Frequency Division multiplexing (OFDM) information and energy to the dedicated LU and several BDs, respectively, while receiving uplink backscattered information from BDs at the same time. One of the key aims in AmBC networks is to ensure fairness among BDs. To address this, we propose the multi-objective Lexicographical optimizationproblem (MLOP), which aims to maximize the minimum BD's throughput while enhancing overall BDs' throughput, subject to the AP's subcarrier power, BDs' reflection coefficients, and backscatter time allocation. Owe to the MLOP is non-convex, we propose Difference Convex Algorithm (DCA) using Exterior Penalty Function Method (EPFM)-an inventive non-convex optimization method- to reach the optimal solution. The most critical advantage of applying this proposed approach is finding the globally optimal solution. The effectiveness of the proposed method supported by theoretical analysis confirms its superiority compared to some of the investigated suboptimal algorithms with the same computational complexity.
With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as ...
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ISBN:
(纸本)9781665491228
With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as the issue of security when implementing communication. In this paper, a virtual antenna array (VAA) is formed in multiple UAV units using collaborative beamforming (CB) technology. Under the interference of multiple eavesdroppers, secure communication with the ground base station (BS) is achieved. To achieve better performance, we formulate a multi-objective optimization problem for UAV network security communication (SCMOP), and jointly optimize the positions and excitation current weights of UAVs to set the null values in the direction of known eavesdroppers, reduce the sidelobe levels (SLLs) and enhance the directivity of the main lobe (ML). Since the formulated SCMOP is an NP-hard problem, we propose an improved the third non-dominated sorting genetic algorithm (IMNSGA-III) with chaos operator and crossover and mutation operators to solve the problem in this paper. The simulation results show that the IMNSGA-III can solve the SCMOP well and obtain the best results compared with other benchmark algorithms.
This paper presents an optimization method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The idea is based on TOPSIS (technique for order preference ...
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This paper presents an optimization method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The idea is based on TOPSIS (technique for order preference by similarity to ideal solution) method. TOPSIS method is a multiple criteria method that identifies a satisfactory solution from a given set of alternatives based on the minimization of distance from an ideal point and maximization of distance from the nadir point simultaneously. A new model of multi-objective bi-level programming problem in an intuitionistic fuzzy environment has been considered. The problem is first reduced to a conventional multi-objective bi-level linear programming problem using accuracy function. Then the modified TOPSIS method is proposed to solve the problem at both the leader and the follower level where various linear/non-linear membership functions are used to represent the flexibility in the approach of decision-makers (DMs). The problem is solved hierarchically, i.e., first the problem at the leader level is solved and then the feasible region is extended by relaxing the decision variables controlled by the leader. The feasible region is extended to obtain a satisfactory solution for the DMs at both levels. Finally, the application of the proposed approach in the production planning of a company has been presented. An illustrative numerical example is also given to explain the methodology defined in this paper.
Non-orthogonal multiple access (NOMA) is regarded as a promising technique for the fifth generation communication due to both its outstanding spectral efficiency (SE) and energy efficiency (EE). In this letter, we inv...
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The constrained multi-objective optimization problems (CMOPs) is widely used in real-world applications and always hard to handle especially when the objective number becomes more or the constraints are too stringent....
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ISBN:
(纸本)9781665404457
The constrained multi-objective optimization problems (CMOPs) is widely used in real-world applications and always hard to handle especially when the objective number becomes more or the constraints are too stringent. In this manuscript, an improved differential evolution method (IDEM) is proposed based on CMOEA/D as well as newly designed mutation operators. Firstly, one mutation operator is presented to improve infeasible points, in which any infeasible point is taken to divide other points into three groups by using the constraint violation information, and based on the division, a potential better point can be found and utilized to improve other infeasible points by the mutation operation. Then the other mutation operator is provided by designing an objective sorting scheme as well as an individual selection method. These two mutation operators are alternately and self-adaptively adopted in evolution process. Finally, the proposed algorithm is executed on some recent benchmark functions and compared with four state-of-the-art EMO algorithms. The experimental results show that IDEM can efficiently solve the CMOPs.
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