In the wake of the increasing integration of cyber-physical systems within power grids, ensuring the security and stability of these systems has become paramount. This paper presents a comprehensive security analysis ...
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In the wake of the increasing integration of cyber-physical systems within power grids, ensuring the security and stability of these systems has become paramount. This paper presents a comprehensive security analysis of cyber-physical power systems (CPPS) employed for frequency control in deregulated power markets. In the era of evolving power grids, characterized by increasing renewable energy penetration and market deregulation, maintaining frequency stability becomes a critical challenge. To address this challenge, this study investigates the synergistic utilization of Unified Power Flow Controller (UPFC) device and Redox Flow Battery (RFB), to enhance the frequency control performance. A realistic two-area CPPS is considered where a cascaded Fractional-Order Proportional-Integral-Derivative-Fractional Integral (FOPID-FI) controller has been implemented for frequency control in both areas. Our analysis delves into the vulnerabilities inherent in CPPS, considering potential cyber threats and their impact on frequency control mechanisms. By employing a systematic approach, we identify critical points of vulnerability in the system architecture and propose countermeasures to mitigate cyber threats effectively. We include extensive simulation results to validate the efficacy of the proposed work by analyzing the performance of the system from vital perspective viz., sensitivity, scalability, robustness, stable, and resilient.
At present, many large and medium-sized cities in China are accelerating the construction of urban rail transit. The contradiction between urban transportation capacity and traffic volume has become increasingly promi...
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Autonomous driving technology is poised to transform transportation systems, yet ensuring reliable and robust autonomous behavior remains a formidable challenge. This paper presents a simulation-based approach leverag...
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Guest Editors Marija Strojnik, Wen Chen, Sarath Gunapala, Joern Helbert, Esteban Vera, and Eric Shirley introduce the Special Section on Advanced Infrared Technology and Remote Sensing Applications II.
Guest Editors Marija Strojnik, Wen Chen, Sarath Gunapala, Joern Helbert, Esteban Vera, and Eric Shirley introduce the Special Section on Advanced Infrared Technology and Remote Sensing Applications II.
Exploration and exploitation are considered essential notions in evolutionary algorithms. However, a precise interpretation of what constitutes exploration or exploitation is clearly lacking and so are specific measur...
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Exploration and exploitation are considered essential notions in evolutionary algorithms. However, a precise interpretation of what constitutes exploration or exploitation is clearly lacking and so are specific measures for characterising such notions. In this paper, we start addressing this issue by presenting new measures that can be used as indicators of the exploitation behaviour of an algorithm. These work by characterising the extent to which available information guides the search. More precisely, they quantify the dependency of a population's activity on the observed fitness values and genetic material, utilising an empirical model that uses a coarse-grained representation of population dynamics and records information about it. The model uses the k-means clustering algorithm to identify the population's "basins of activity". The exploitation behaviour is then captured by an entropy-based measure based on the model that quantifies the strength of the association between a population's activity distribution and the observed fitness landscape information. In experiments, we analysed the effects of the search operators and their parameter settings on the collective dynamic behaviour of populations. We also analysed the effect of using different problems on algorithm *** define a behavioural landscape for each problem to identify the appropriate behaviour to achieve good results and point out possible applications for the proposed model.
Differential evolution (DE) stands out as a prominent algorithm for addressing global optimization challenges. The efficacy of DE hinges crucially upon its mutation operation, which serves as a pivotal mechanism in ge...
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Differential evolution (DE) stands out as a prominent algorithm for addressing global optimization challenges. The efficacy of DE hinges crucially upon its mutation operation, which serves as a pivotal mechanism in generating diverse and high-quality solutions. This article explores various mutation operations aimed at augmenting the performance of DE in global optimization tasks. A distinct mutation strategy is introduced, with the primary objective of achieving a harmonious equilibrium between exploration and exploitation to enhance both convergence speed and solution quality. The proposed DE centres on a novel mutation-based strategy, introducing a new coefficient factor ("sigma") in conjunction with the base vector of the basic mutation strategy ("DE/rand/1"). This innovation aims to fortify the convergence of local variables during exploitation, thereby improving both the convergence rate and quality. The effectiveness of the proposed mutation operations is evaluated across a set of 27 benchmark functions commonly employed in global optimization. Experimental results conclusively demonstrate that these enhanced mutation strategies significantly outperform state-of-the-art algorithms in terms of solution accuracy and convergence speed. This study underscores the critical role of mutation operations in DE and provides valuable insights for designing more potent mutation strategies to tackle complex global optimization problems.
Dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer le...
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Dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer learning (TL) has emerged the empirical knowledge and is an effective approach to solve DMOPs. However, negative transfer can occur when the transfer method is not suitable for the transfer task. It may deviate the search path and seriously reduce the efficiency, so how to reduce the occurrence of negative transfer to save the running time of dynamic multi-objective evolutionary algorithm (DMOEA) is an important issue to be addressed. A division-selection transfer learning evolutionary algorithm for dynamic multi-objective optimization (DST-DMOEA) is designed towards this aim. Specifically, individuals with high Spearman correlation are relatively stable in different environments, selecting them to train the Support Vector Regression (SVR) model ensures a more accurate capture of solution features, predicting the objective values of historical solutions based on the model, and thus divide historical solutions into elite and non-elite solutions. Subsequently, for the elite solutions, individual TL that incorporates local information for optimization and transfer is used, while the non-elite solutions are handled with manifold TL method to obtain the overall data distribution and understand the internal structure. Then, merge the predicted individuals generated by two parts of TL will constitute as the initial population in the optimization process. Compared with other algorithms, the initial solution of DST-DMOEA is closer to the real POF, effectively reducing negative transfer. In addition, in 51 test instances, DST-DMOEA has shown superior performance in over 30 instances.
In this paper, a knowledge based artificial neural network (KBANN) assisted evolutionary algorithm (EA) is presented for optimization of eye diagram characteristics of on-chip multi-layered graphene nanoribbon (MLGNR)...
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ISBN:
(纸本)9798350383768
In this paper, a knowledge based artificial neural network (KBANN) assisted evolutionary algorithm (EA) is presented for optimization of eye diagram characteristics of on-chip multi-layered graphene nanoribbon (MLGNR) interconnect network driven with nanosheet FET (NSFET) inverters. First, a KBANN model is trained to mimic the eye diagram characteristics of the MLGNR interconnect network. The next step is to use particle swarm optimization (PSO) and EA (such as strength pareto evolutionary algorithm (SPEA2)) for optimizing the eye diagram characteristics obtained from the outputs of the KBANN.
Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing ...
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Monte Carlo Tree Search (MCTS) is a best-first sampling/planning method used to find optimal decisions. The effectiveness of MCTS depends on the construction of its statistical tree, with the selection policy playing a crucial role. A particularly effective selection policy in MCTS is the Upper Confidence Bounds for Trees (UCT). While MCTS/UCT generally performs well, there may be variants that outperform it, leading to efforts to evolve selection policies for use in MCTS. However, these efforts have often been limited in their ability to demonstrate when these evolved policies might be beneficial. They frequently rely on single, poorly understood problems or on new methods that are not fully comprehended. To address these limitations, we use three evolutionary-inspired methods: evolutionary Algorithm (EA)-MCTS, Semantically-inspired EA (SIEA)-MCTS as well as Self-adaptive (SA)-MCTS, which evolve online selection policies to be used in place of UCT. We compare these three methods against five variants of the standard MCTS on ten test functions of varying complexity and nature, including unimodal, multimodal, and deceptive features. By using well-defined metrics, we demonstrate how the evolution of MCTS/UCT can yield benefits in multimodal and deceptive scenarios, while MCTS/UCT remains robust across all functions used in this work.
The optimisation of multimodal transportation is constantly evolving, striving to provide commuters with seamless mobility and sustainable networks. Multimodal transportation problems often, however, present optimisat...
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The optimisation of multimodal transportation is constantly evolving, striving to provide commuters with seamless mobility and sustainable networks. Multimodal transportation problems often, however, present optimisation challenges because of their high dimensionality, compounded by network size, modelling criteria, and modes. Among these challenges is computational complexity, which can be reduced with the use of metaheuristic solution approaches that strive to find an acceptable solution within a reasonable timeframe. In addition, as machine learning finds integration within real-world applications, the demand for parallel computing and robust computational infrastructure is on the rise. Given these rapid shifts, this paper is motivated to present a comprehensive systematic literature review on the optimisation of multimodal transportation, focusing on the urban mobility of passengers, using metaheuristics. After conducting a systematic bibliographic search, a thorough classification of studies based on their problem scope, mathematical formulation, methodology, temporal- and network settings is conducted. Overall, findings provide insights into tackling the challenges of multimodal urban transport optimisation for future investigation, addressing concerns over scalability and efficiency for real-time deployment.
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