For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are pro...
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For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and conver-gence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.
Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares th...
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Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares that are stacked over one another in order to reconstruct back the original image accurately. The major limitation that existed in the traditional VC techniques is pixel expansion, in which pixel expansion is replaced with a number of sub-pixels in individual share, which causes a considerable impact on the contrast and resolution of the image that further gradually decreases the quality of the image. VC is named for its essential characteristics, such as transmitting the images with two or more shares with an equal number of black pixels and color pixel distribution. The secret message can be decrypted using Human Visual System (HVS). In this paper, 50 research papers are reviewed based on various classification algorithms, which are effectively used for the VC technique. The classification algorithms are categorized into three types, namely, meta-heuristic, heuristic, and evolutionary, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is done based on the existing research works by considering the classification algorithms, tools, and evaluation metrics.
In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also em...
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In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also emphasizing the environmental impact. In order to measure this impact, the key parameter is the CO2 emission in the atmosphere. The most powerful mean to satisfy this compromise between economic benefits and emission decrease is represented by the concept of Smart Grid. A Smart Grid implies a joint participation between information network and electric grid. In order to acquire the data from the electric grid, transmit them through the IT network, compute and translate them into commands to the plant devices, an 'intelligent brain' is necessary. In order to embed a small local network in the larger VPP a delocalized intelligent device is necessary, able to interface with the Smart Grid. An optimization algorithm performs this function of intelligent delocalized brain by setting different set-points for the energy devices on field. In this paper a purposefully developed optimization algorithm is described, with the aim of optimizing the operations of an existent trigeneration plant managing both RES and fossil energy sources. The plant analysed is a real plant located in central Italy, provided by several generators (PV, CHP, absorption chiller, electric chiller, gas boiler and a wind turbine). The results are yielded by a MATLAB/Simulink simulation tool, where all plant devices are characterized by datasheet information and on-field measurements. The benefits evaluation of the algorithm optimized management is obtained by embedding inside Simulink the optimization logic and executing it during the simulation runtime. The performance is compared with conventional thermal led management operations simulated in the same platform. The comparison is mainly based on economic costs but also considers CO2 emissions and primary energy consumption. The analysis tak
The standard localization approach is characterized by a fixed position distribution of the anchor nodes, which cannot be dynamically modified based on the deployment environment. This paper proposes a novel approach ...
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The standard localization approach is characterized by a fixed position distribution of the anchor nodes, which cannot be dynamically modified based on the deployment environment. This paper proposes a novel approach combining Radial Bias (RB) with the Seeker optimization algorithm (SOA) to address the challenges of energy-constrained target localization and tracking. The RB technique enhances localization accuracy by refining the position estimates of the target, while the SOA optimizes sensor deployment and data transmission paths to minimize energy consumption. By integrating these two methodologies, ensures a balance between precision in tracking and energy efficiency. Extensive simulations shown this technique surpasses existing methods in terms of both accuracy in determining the location and the duration of network operation. This makes it attractive option for applications of energy-constrained WSNs. The investigation examines the outcome of the particle count in the RBSO algorithm, specifically for values of 5, 10, 15, 20, and 25. The simulation results show that the recommended strategy decreases particles, speeds up positioning and tracking, and maintains target localization and tracking accuracy. It is seen that the proposed RadB_SOA achieves 12.4 % of transmission error, 14.6 % of ranging error, 96.3 % of localization coverage, 98.65 % of PDR, and 21.56 % of energy consumption. center dot The Radial Bias-Seeker optimization algorithm (RadB_SOA) suggested enhances the precision in target localization and optimizes energy usage in wireless sensor networks. center dot Simulation outcomes reveal improved tracking accuracy, minimized transmission and ranging errors, as well as increased localization coverage over current techniques. center dot The research presents an extensive evaluation of particle count fluctuations in RBSO, demonstrating enhanced positioning speed and precision with network efficiency.
In the field of reliability engineering, importance measures are widely used to prioritize components within a system and facilitate the improvement of system performance. However, current multi-component importance m...
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In the field of reliability engineering, importance measures are widely used to prioritize components within a system and facilitate the improvement of system performance. However, current multi-component importance measures, such as joint reliability importances (JRIs) and their extensions, do not fully account for the potential impact of limited resource constraints, which can significantly impede efforts to improve system reliability. To address this issue, this paper proposes a novel JRI of two components for the cost-constrained reliability opti-mization model (ROM), which incorporates constraint factors into the JRI calculation. This new JRI can be used to evaluate the interaction effect of two components on system reliability under cost constraints. Subsequently, a cost-constrained, ROM-based, mixed reliability importance (CRMRI) is introduced by integrating the features of single-component importance measures with the newly devised JRI. Given equivalent costs for improving each component, the CRMRI approach can identify the two components whose simultaneous improvement contributes the most to enhancing system reliability. Lastly, we develop a CRMRI-based genetic algorithm (CRMGA) to solve the cost-constrained ROM. Experimental results on systems of various scales demonstrate that CRMGA can produce superior solutions with faster convergence speed, enhanced robustness, and higher efficiency compared to other optimization algorithms.
Reliability is a parameter of evaluating network performance and expected path length can index the contribution of s-t paths to network reliability. It is meaningful to observe the important part of network performan...
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ISBN:
(纸本)9781467390262
Reliability is a parameter of evaluating network performance and expected path length can index the contribution of s-t paths to network reliability. It is meaningful to observe the important part of network performance in light of the reliability and path length. In this paper, we attempt to reveal the important part of network performance based on reliability. Conversely we consider the optimization problem of two-terminal reliability with expected-path length constraint. Next, we transform the problem into searching delta-maximum graph, in which its expected path length is not greater than delta(0) and reliability is maximum. Further, we find a rule of removing redundant subgraphs and propose an algorithm to search the optimal solution. Simulation shows the effectiveness of the proposed algorithm.
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated a...
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ISBN:
(纸本)9781467374439
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated as nonlinear optimization problems. A chaos Particle Swarm optimization(PSO) algorithm combined with multi-agent scheme is proposed for the thrust allocation in this paper. The algorithm which uses multi-agent topological structure has three functions that keeps the diversity of the particle swarm population, improving particle swarm global search ability, and enhancing information diversity. Relying on chaotic local search to get rid of local optima, it can also improve the convergence precision. The numerical simulations are conducted to demonstrate the effectiveness of the proposed methods, and the results are compared with PSO algorithm.
In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor network...
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ISBN:
(纸本)9783662469811;9783662469804
In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor networks (LOA_MMSN) is proposed. LOA_MMSN analyzes the constraints, establishes network optimization model, and decomposes the model into movement path selection model and lifetime optimization model with known movement paths. Finally, the two models are solved. Simulation results show that LOA_MMSN can extend the network lifetime, balance node energy consumption and reduce data gathering latency. Under certain conditions, it outper forms Ratio_w, TPGF and lifetime optimization algorithm with single mobile sink node for WSNs.
This paper attempts to find an optimization algorithm to reduce number of iterations required for a node to be searched in a MultiCast Tee network. After effectively searching the node, it applies this technique in cl...
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ISBN:
(纸本)9781479976782
This paper attempts to find an optimization algorithm to reduce number of iterations required for a node to be searched in a MultiCast Tee network. After effectively searching the node, it applies this technique in cloud computing, more exactly, cloud information security so that data is safely transferred from one user to another, without any hindrance. The basic idea to incorporate the concept of MultiCast Tree is because it always supports reliable transmission of information, if by any chance, there is a link failure, it does not reduce the availability of resources, nor does it increase any delay in transmission of information, and at the same time minimizes work load. Thus, we can effectively use it in cloud computing, which is nothing but provision of resources to a pool of users through some kind of an interface, for example, Internet. While monitoring the distributed foundation establishment of cloud computing, it becomes extremely important to take into consideration the security of the information provided by users. If we use a vast number of nodes or links, it becomes practically impossible to trace the reliability of transmission, and which might result in important information getting lost. Hence to avoid such a consequence, it becomes necessary to optimize the number of these links, so that it becomes easier to search them in a minimum amount of time. Our main attempt has been to optimize these links and effectively use it in a communication network which has characteristics of a MultiCast Tree incorporated in it. This not only helps in efficient use of cloud computing system, but also ensures users that their data is in safe hands.
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node pote...
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ISBN:
(纸本)9789881563897
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node potential as design variables, and equilibrium relation between node potential and sub-circuit current as frame-objective function, dynamic design variables optimization algorithm analysis of arbitrary complicated sinusoidal steady-state circuit network is proposed. Universal program computing sub-circuit current and node potential is completed. Practical examples are computed. Effectiveness and feasibility is verified. A new clue is set up for computing complicated alternating-current circuit network rapidly and precisely.
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