Recognizing critical nodes in complex networks has emerged as a challenging task across several application areas. The critical node detection problem (CNDP) is an optimization challenge that entails determining the s...
详细信息
Recognizing critical nodes in complex networks has emerged as a challenging task across several application areas. The critical node detection problem (CNDP) is an optimization challenge that entails determining the subset of nodes whose removal adversely affects network connectivity and performance based on certain predetermined criteria. The problem of recognizing critical nodes has received significant consideration since it is a vital challenge in a multitude of application areas. As a result, many variants have been proposed on the basis of numerous metrics. In this survey, we discuss different applications, challenges, and solutions to single- and multi-objective CNDP. We review and classify different recent advancements and obtained outcomes for each variant, proposed from 2017 to 2022. To our best knowledge, this is the first survey on the heuristic optimization-based solutions for CNDP that have been developed in recent years. This study also provides researchers with future insight into filling gaps in the critical nodes research field and identifying emerging research trends in this area.
A methodology to evaluate transmission project profitability including reliability optimization utilizing an evolutionary algorithm is developed in this paper. The methodology uses optimal assignment of shunt compensa...
详细信息
A methodology to evaluate transmission project profitability including reliability optimization utilizing an evolutionary algorithm is developed in this paper. The methodology uses optimal assignment of shunt compensation in nodes and parallel redundancy in lines with the highest participation in stationary voltage instability subject to economic constrains. The minimum singular value (MSV) technique utilizing the load flow reduced Jacobian matrix of the system is utilized to evaluate stationary voltage instability. An additional concept of costumer damage function is included to consider the reliability worth in electric tariff to justify economically the transmission project. The methodology is applied to the northern area of the Mexican grid. (C) 2006 Elsevier Ltd. All rights reserved.
The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include ...
详细信息
The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.
Log-periodic antenna is a special antenna type utilized with great success in many broad-band applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively ...
详细信息
Log-periodic antenna is a special antenna type utilized with great success in many broad-band applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively used in electromagnetic compatibility measurements, spectrum monitoring and TV reception. In this study, a log-periodic dipole array is measured, simulated, and then optimized in the 470-860 MHz frequency band. Two simulations of the antenna are initially performed in time and frequency domain respectively. The comparison between these simulations is presented to ensure accurate modelling of the antenna. The practically measured realized gain is in good agreement with the simulated realized gain. The antenna is then optimized to concurrently improve voltage standing wave ratio, realized gain and front-to-back ratio. The optimization process has been implemented by using various algorithms included in CST Microwave Studio, such as Trusted Region Framework, Nelder Mead Simplex algorithm, Classic Powell and Covariance Matrix Adaptation evolutionary Strategy. The Trusted Region Framework algorithm seems to have the best performance in adequately optimizing all predefined goals specified for the antenna.
In the last decade, several algorithms have been proposed to solve the problem of community detection in complex networks. Many of them are based on swarm intelligence and evolutionary algorithms. Most of these algori...
详细信息
In the last decade, several algorithms have been proposed to solve the problem of community detection in complex networks. Many of them are based on swarm intelligence and evolutionary algorithms. Most of these algorithms use the modularity density as a fitness function to maximize. However, these algorithms attempt to find the best solution without taking into consideration the structure of the network. In this paper, a new discrete modified Fireworks Algorithm (FWA) has been developed to solve the problem of community detection. A new initialization strategy and new mutation strategies are proposed, based on the label propagation strategy to enhance the algorithm and to speed up its convergence. The proposed algorithm has been evaluated on real-world and synthetic networks. Experimental results compared with three other known algorithms show the effectiveness of using our proposed algorithm for solving the problem of detecting communities in complex networks.
This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic m...
详细信息
This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic multiobjective problems. In particular, the decision variables are divided into two groups: convergence variables (CS) and diversity variables (DS), and different strategies are introduced to optimize these groups. Two kinds of subpopulations are used in the proposed algorithm, i.e., the subpopulations that represent DS and the subpopulations that represent CS. In the evolution process, the coevolution of DS and CS is carried out through genetic operators, and subpopulations of CS are gradually merged into DS, which is optimized in the global search space, based on an indicator to avoid becoming trapped in local optimum. Once a change is detected, a prediction method and a diversity introduction approach are adopted for these two kinds of variables to get a promising population with good diversity and convergence in the new environment. The proposed algorithm is tested on 16 benchmark dynamic multiobjective optimization problems, in comparison with state-of-the-art algorithms. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization. (C) 2021 Elsevier Inc. All rights reserved.
Identification, localization and quantification of structural damage can be performed through a model-updating procedure. Model-updating methods require a baseline finite element (FE) model of the undamaged structure,...
详细信息
Identification, localization and quantification of structural damage can be performed through a model-updating procedure. Model-updating methods require a baseline finite element (FE) model of the undamaged structure, which imposes a restriction on their applicability and can become very problematic especially for large and complex civil structures. Modeling errors in the baseline model whose effects exceed the modal sensitivity to damage are critical and make an accurate estimation of damage impossible. This paper presents an identification algorithm using modal data for assessing structural damage that is based on FE-updating procedures and takes modeling error into account. To overcome its influence, differences of mode shapes and frequencies before and after damage for both numerical model and experimental measurements are used instead of the mode shapes and frequencies themselves. To formulate the objective function, two different approaches have been considered taking into account how these differences are grouped: a single-objective approach and a multiobjective approach. The effectiveness of both approaches is verified against numerical and experimental results. (C) 2008 Elsevier Ltd. All rights reserved.
All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter val...
详细信息
All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.
In the highest-accuracy mixture models available today, these being the multi-fluid Helmholtz-energy explicit formulations, there are a number of binary interaction parameters that must be obtained through correlation...
详细信息
In the highest-accuracy mixture models available today, these being the multi-fluid Helmholtz-energy explicit formulations, there are a number of binary interaction parameters that must be obtained through correlation or estimation schemes. These binary interaction parameters are used to shape the thermodynamic surface and yield higher fidelity predictions of various thermodynamic properties including vapor-liquid equilibria and homogeneous p-v-T data, among others. In this work, we have used a novel and entirely automatic evolutionary optimization algorithm written in the python programming language to fit the two most important interaction parameters for more than 1100 binary mixtures. This fitting algorithm can be run on multiple processors in parallel, resulting in a reasonable total running time for this large set of binary mixtures. For more than 830 of the binary pairs, the median absolute relative error in bubble-point pressure is less than 5%. The source code for the fitter is provided as supplemental data, as well as the entire set of binary interaction parameters obtained and comparisons with the best experimental vapor-liquid-equilibrium data that are available.
Having a mechanism to mathematically model the problem of the optimal allocation of parking spots within cities could bring great benefits to society. According to the International Parking Institute, about 38% of the...
详细信息
Having a mechanism to mathematically model the problem of the optimal allocation of parking spots within cities could bring great benefits to society. According to the International Parking Institute, about 38% of the cars circulating throughout a city are looking for available parking spots, leading to increased pollution and subsequent health problems, as well as economic losses due to wasted man-hours. In the work presented here, a new mathematical model describing the problem of the optimal allocation of parking spots is proposed, along with an evolutionary algorithm to demonstrate how this model can be used in practice. A simulated annealing algorithm was implemented to test the effectiveness of this approach. The proposed strategy will allow users to find parking more quickly and easily, as well as lead to new services for the hot-topic of smart mobility. For the definition of the problem, a real map of the city of Malaga, Spain, was used along with Sumo software to carry out the simulations. The results clearly demonstrated that the proposed mechanism is capable of minimising the global cost of parking, implying a direct benefit for users.
暂无评论