With the continuous progress of power system and power electronics technology, multiple power electronic devices play an increasingly important role in power systems. Due to the variety and large number of source-side...
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The improvement of automated controlsystems in various fields of human activity is the main direction of the development of Society The problem of planning a set of jobs performed using two types of resources - renew...
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Artificial bee colony (ABC) algorithm is often challenged by slow convergence, poor accuracy, and premature convergence in handling complex medium-scale optimization problems, due to its biased search equation and the...
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ISBN:
(纸本)9798350377859;9798350377842
Artificial bee colony (ABC) algorithm is often challenged by slow convergence, poor accuracy, and premature convergence in handling complex medium-scale optimization problems, due to its biased search equation and the high assimilation rate of bees within the colony. To trade off the ABC for global exploration and local exploitation of complex problem landscapes, this paper proposes an enhanced ABC based on elimination history and elite correction (HeCABC). Given the bias effects of the superior solutions and the historical inferior solutions eliminated on the search behavior of ABC, HeCABC separately formulates an exploration equation oriented by the historically eliminated inferior solutions and an exploitation equation upon multi-elite information fusion for employed bees and onlook bees, to regulate their exploration and exploitation intensity of the solution space. Meanwhile, HeCABC couples an elite correction strategy for fine-tuning the quality of the elites based on the update signal of these elites within the colony. HeCABC is experimented on various complex CEC 2014 test functions of 30 dimensions. The experimental results showcase its superior performance over five state-of-the-art ABC variants and two advanced swarm optimizers.
The feedback control system, which is closed loop through the real time network, is called the networked control system. Because of the existence of the control loop network, the analysis and design of the networked c...
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The paper proposes the typical model and the functional structure of reflexive-active systems of artificial heterogeneous intelligent agents within the multi-agent approach to the construction of distributed artificia...
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This article provides a comparative analysis of gradient descent and natural gradient descent in its own implementation in terms of accuracy and convergence rate on various data sets. The results of the experiments ob...
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In the modern world, any development is impossible without the development of new high-tech automated systems in various subject areas. In this study, three different algorithms were considered that use effective moti...
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This article fits into the complex dynamics of Anti-lock Braking systems (ABS) and the management of tire slide in current cars. ABS, a critical safety component, keeps wheels from locking when braking, maintaining ma...
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Evolutionary algorithms (EAs) simulate the process of biological evolution in nature to solve problems. EAs are a class of global optimization methods with high robustness, and have been widely applied in many areas. ...
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ISBN:
(纸本)9798350377859;9798350377842
Evolutionary algorithms (EAs) simulate the process of biological evolution in nature to solve problems. EAs are a class of global optimization methods with high robustness, and have been widely applied in many areas. Feedback control (FC) regulates the behaviors of the system based on feedback. To understand the insight of EAs better and analyze the reported EAs, this paper proposes an entropy feedback based evolutionary algorithms (EFEAs). The entropy is adopted to measure the evolutionary state and acts as the feedback for self-adaptive adjustment. Based on the feedback of entropy, the control parameters, such as ranking selection probability, crossover probability, and mutation probability, are adaptive adjusted to tune the evolutionary state and improve the search performance of EAs. Finally, the state feedback framework is applied to analyze the mechanism and performance of the improved EAs. EFEAs provides a new perspective for understanding the mechanisms of EAs and a new approach to improve the performance of EAs. Experiments in the Travelling Salesman Problem (TSP) show that EFEAs outperforms other comparison algorithms.
Modern control structures, based on PLC (Programmable Logic controller) and SCADA (Supervisory control and Data Acquisition) systems are reliable, have great programming flexibility, enable changes in basic parameters...
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