In this study, we present a Pareto-based chemicalreaction optimization(PCRO)a* algorithm for solving the multiarea environmental/economic dispatch optimization *** objectives are minimized simultaneously, i.e., total fu...
详细信息
In this study, we present a Pareto-based chemicalreaction optimization(PCRO)a* algorithm for solving the multiarea environmental/economic dispatch optimization *** objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposeda* algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposeda* algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable thea* algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposeda* algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCROa* algorithm is favorably compared with severala* algorithms, with regards to both solution quality and diversity.
As a typical technology for optical encryption,phase retrievala* algorithms have been widely used in optical information encryption and authentication *** paper presents three applications of two-dimensional(2D)phase re...
详细信息
As a typical technology for optical encryption,phase retrievala* algorithms have been widely used in optical information encryption and authentication *** paper presents three applications of two-dimensional(2D)phase retrieval for optical encryption and authentication:first,a hierarchical image encryption system,by which multiple images can be hidden into cascaded multiple phase masks;second,a multilevel image authentication system,which combines(t,n)threshold secret sharing(both t and n are positive integers,and t≤n)and phase retrieval,and provides both high-level and low-level authentication;and finally,a hierarchical multilevel authentication system that combines the secret sharing scheme based on basic vector operations and the phase retrieval,by which more certification images can be encoded into multiple cascaded phase masks of different hierarchical *** three phase retrievala* algorithms can effectively illustrate phase-retrievalbased optical information *** principles and features of each phase-retrieval-based optical security method are analyzed and *** is hoped that this review will illustrate the current development of phase retrievala* algorithms for optical information security and will also shed light on the future development of phase retrievala* algorithms for optical information security.
An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is ado...
详细信息
An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimizationa* algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iterationa* algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposeda* algorithm. Two numerical experiments anda* algorithm analyses were performed to illustrate the optimizationa* algorithm in detail.
Some classical penalty functiona* algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization *** this ...
详细信息
Some classical penalty functiona* algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization *** this paper,a novel penalty function(called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is *** on the M-objective penalty function,ana* algorithm is developed to solve an optimal solution to the inequality constrained optimization problems,with its convergence proved under some ***,numerical results show that the proposeda* algorithm has a much better convergence than the classical penalty functiona* algorithms under big penalty parameters,and is efficient in choosing a penalty parameter in a large range in Matlab software.
The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network ...
详细信息
The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved fireflya* algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical fireflya* algorithm(FA) and four recently proposed FA variants.
The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as backg...
详细信息
The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and *** this study,an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit *** applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses.
In this paper, a memetica* algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used t...
详细信息
In this paper, a memetica* algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposeda* algorithm is more effective and efficient than the existing methods in solving the CGVRP.
The capacitated vehicle routing problem (CVRP), which aims at minimizing travel costs, is a wellknown NP-hard combinatorial optimization. Owing to its hardness, many heuristic searcha* algorithms have been proposed to t...
详细信息
The capacitated vehicle routing problem (CVRP), which aims at minimizing travel costs, is a wellknown NP-hard combinatorial optimization. Owing to its hardness, many heuristic searcha* algorithms have been proposed to tackle this problem. This paper explores a recently proposed heuristica* algorithm named the fireworksa* algorithm (FWA), which is a swarm intelligencea* algorithm. We adopt FWA for the combinatorial CVRP problem with several modifications of the original FWA: it employs a new method to generate "sparks" according to the selection rule, and it uses a new method to determine the explosion amplitude for each firework. The proposeda* algorithm is compared with several heuristic search methods on some classical benchmark CVRP instances. The experimental results show a promising performance of the proposed method. We also discuss the strengths and weaknesses of oura* algorithm in contrast to traditionala* algorithms.
A system model is formulated as the maximization of a total utility function to achieve fair downlink data scheduling in multiuser orthogonal frequency division multiplexing （OFDM） wireless networks. A dynamic subca...
详细信息
A system model is formulated as the maximization of a total utility function to achieve fair downlink data scheduling in multiuser orthogonal frequency division multiplexing （OFDM） wireless networks. A dynamic subcarrier allocationa* algorithm （DSAA） is proposed, to optimize the system model. The subcarrier allocation decision is made by the proposed DSAA according to the maximum value of total utility function with respect to the queue mean waiting time. Simulation results demonstrate that compared to the conventionala* algorithms, the proposeda* algorithm has better delay performance and can provide fairness under different loads by using different utility functions.
Introduction and Objectives: TIPS placement is an effective, possibly life-saving, treatment for complications of portal hypertension. The pressure shift induced by the stent can lead to cardiac decompensation (CD). W...
详细信息
Introduction and Objectives: TIPS placement is an effective, possibly life-saving, treatment for complications of portal hypertension. The pressure shift induced by the stent can lead to cardiac decompensation (CD). We investigated the incidence of CD, possible variables associated with CD and the validity of the Toulousea* algorithm for risk prediction of CD post-TIPS. Patients and Methods: A total of 106 patients receiving TIPS for variceal bleeding (VB, 41.5%) or refractory ascites (RA, 58.5%) with available echocardiography and NT-proBNP results were included and retrospectively reviewed. Development of CD between time of TIPS placement and occurrence of liver transplantation, death or loss-to-follow-up was recorded. Competing risk regression analysis was performed to assess which baseline variables predicted occurrence of CD post-TIPS. Results: A total of 12 patients (11.3%) developed CD after a median of 11.5 days (IQR 4 to 56.5) post-TIPS. Multivariate regression showed age (HR 1.06, p = 0.019), albumin (HR 1.10, p = 0.009) and NT-proBNP (HR 1.00, p = 0.023) at baseline predicted CD in the RA group. No clear predictors were found in those receiving TIPS for VB. Correspondingly, the Toulousea* algorithm successfully identified patients at risk for CD, however only in the RA population (zero risk 0% vs. low risk 12.5% vs. high risk 35.3% with CD;p = 0.003). Conclusions: CD is not an infrequent complication post-TIPS occurring in 1/10 patients. The Toulousea* algorithm can identify patients at risk of CD, though only in patients receiving TIPS for RA. Allocation to the highrisk category warrants close monitoring but should not preclude TIPS placement. (c) 2024 Fundaci & oacute;n Cl & iacute;nica M & eacute;dica Sur, A.C. Published by Elsevier Espa & ntilde;a, S.L.U. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)
暂无评论