The typemixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization mo...
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The typemixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.
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...
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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 allocation 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 conventional algorithms, the proposed algorithm has better delay performance and can provide fairness under different loads by using different utility functions.
The fully developed turbulence can be regarded as a nonlinear system,with wave coupling inside,which causes the nonlinear energy to transfer,and drives the turbulence to develop further or be *** analysis is one of th...
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The fully developed turbulence can be regarded as a nonlinear system,with wave coupling inside,which causes the nonlinear energy to transfer,and drives the turbulence to develop further or be *** analysis is one of the most effective methods to study turbulence *** order to apply it to the study of the nonlinear wave coupling process of edge plasma turbulence,an efficient algorithm based on spectral analysis technology is proposed to solve the nonlinear wave coupling *** algorithm is based on a mandatory temporal static condition with the nonideal spectra separated from the ideal *** realization idea and programing flow are *** to the characteristics of plasma turbulence,the simulation data are constructed and used to verify the algorithm and its implementation *** simulation results and experimental results show the accuracy of the algorithm and the corresponding program,which can play a great role in the studying the energy transfer in edge plasma *** an application,the energy cascade analysis of typical edge plasma turbulence is carried out by using the results of a case ***,a physical picture of the energy transfer in a kind of fully developed turbulence is constructed,which confirms that the energy transfer in this turbulent system develops from lower-frequency region to higher-frequency region and from linear growing wave to damping wave.
Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road ***,in real cases,especially in the era of smart cities where many kinds of trans...
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Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road ***,in real cases,especially in the era of smart cities where many kinds of transportation-related data become easily available,recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria,e.g.,finding the route with the highest accumulated scenic score along(utility)while not exceeding the given travel time budget(cost).Such problem can be viewed as a variant of arc orienteering problem(AOP),which is well-known as an NP-hard *** this paper,targeting a more realistic AOP,we allow both scenic score(utility)and travel time(cost)values on each arc of the road network are time-dependent(2TD-AOP),and propose a memetic algorithm to solve *** be more specific,within the given travel time budget,in the phase of initiation,for each population,we iteratively add suitable arcs with high scenic score and build a path from the origin to the destination via a complicate procedure consisting of search region narrowing,chromosome encoding and *** the phase of the local search,each path is improved via chromosome selection,local-improvement-based mutation and crossover ***,we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively,and the experimental results demonstrate that it outperforms the baselines.
A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model op...
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A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.
Temperature control is the key of Ruhrstahl-Heraeus (RH) process in steelmaking plant. The accuracy of RH control model greatly affects the molten steel temperature fluctuation. To obtain RH control model with higher ...
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Temperature control is the key of Ruhrstahl-Heraeus (RH) process in steelmaking plant. The accuracy of RH control model greatly affects the molten steel temperature fluctuation. To obtain RH control model with higher accuracy, an improved case-based reasoning (CBR) model based on attribute weights optimized by genetic algorithm (GA) was proposed. The fitness function in GA was determined according to the prediction accuracy of end temperature of molten steel in RH;then, GA is used to optimize all the attribute weights based on known case base. An improved CBR model that contains the optimized attribute weights was applied to predict end temperature of molten steel in RH, and the prediction accuracy wascalculated. Four methods, CBR based on attribute weights optimized by GA (CBR-GA), ordinary CBR, back propagation neural network (BPNN) and multiple linear regression (MLR) method were employed for comparison. The results show that in the error range of [- 3 ℃, 3 ℃],[- 5 ℃, 5 ℃],[- 7 ℃, 7 ℃] and [- 10 ℃, 10 ℃], the prediction accuracy of CBR-GA was improved by 19.99%, 28.19%, 27.11% and 16.3%, respectively, than that of MLR. Compared with BPNN, the prediction accuracy increased by 3.22%, 7.44%, 5.29% and 2.40%, respectively. Compared with ordinary CBR, the accuracy increased by 5.43%, 5.80%, 4.66% and 2.27%, respectively.
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...
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Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discret...
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An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discrete constraints in the original problem with one single continuous constraint. A feasible direction method is designed to solve the resulting nonlinear programming problem. The method employs only the gradient evaluations of the objective function, and no any matrix calculations and no line searches are required. This greatly reduces the calculation cost of the method, and is suitable for the solution of large size max-cut problems. The convergence properties of the proposed method to KKT points of the nonlinear programming are analyzed. If the solution obtained by the proposed method is a global solution of the nonlinear programming problem, the solution will provide an upper bound on the max-cut value. Then an approximate solution to the max-cut problem is generated from the solution of the nonlinear programming and provides a lower bound on the max-cut value. Numerical experiments and comparisons on some max-cut test problems （small and large size） show that the proposed algorithm is efficient to get the exact solutions for all small test problems andwell satisfied solutions for most of the large size test problems with less calculation costs.
In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum ...
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In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum algorithm *** this survey as well as tutorial article, the authors ?rst present an overview of the development of quantum algorithms, then investigate ?ve important techniques: Quantum phase estimation, linear combination of unitaries, quantum linear solver, Grover search, and quantum walk, together with their applications in quantum state preparation, quantum machine learning, and quantum search. In the end, the authors collect some open problems in?uencing the development of future quantum algorithms.
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