This paper proposed an improved Stud Genetic algorithm using the Opposition-based strategy(SGAO) to improve the performance of the traditional SGA and accelerate its convergence *** SGAO,we use opposition-based approa...
详细信息
ISBN:
(纸本)9781510823808
This paper proposed an improved Stud Genetic algorithm using the Opposition-based strategy(SGAO) to improve the performance of the traditional SGA and accelerate its convergence *** SGAO,we use opposition-based approach to initialize the population and to perform mutation with the aim to improve the quality of *** experiments,we use some benchmark functions to the show the performance of the proposed approach and compare it with other algorithms such as genetic algorithm,different evolutionary,particle swarm optimization and stud genetic *** show that SGAO has faster convergence speed and higher solution precision.
Transportation facility or automotive service enterprise location is an interesting and important issue. To improve transportation efficiency, many researchers have addressed the traditional facility location allocati...
详细信息
Transportation facility or automotive service enterprise location is an interesting and important issue. To improve transportation efficiency, many researchers have addressed the traditional facility location allocation (FLA) problem, e.g., the FLA problem with the minimum transportation cost or the maximum obtained profit. However, with the improvement of saving energy awareness and the enhancement of environmental concerns, energy efficient and low carbon emission should be considered as key factors influencing the FLA problem. To handle this issue via a more practical method, this work proposes a sustainable location issue for an automotive service enterprise. That is, by taking the vehicle inspection station as a typical automotive service enterprise and an example, this work presents new energy-efficient models of its sustainable location with carbon constraints. An artificial fish swarm algorithm is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and testify the effectiveness of the algorithm. Note to Practitioners-This work concerns the sustainable management problem for locating an automotive service enterprise. To deal with such an issue, this work proposes a novel sustainable analysis method. The previous research handles such a problem through a methodology based on the traditional economical method, which is not enough without considering sustainable factors, e.g., energy consumption and carbon emission. The goal of this work is to establish the energy-efficient models for locating an automotive service enterprise with carbon emission constraint, i.e., to meet the specific requirement of carbon emission assuredly while minimizing the transportation energy/fuel consumption of service customers. Both theoretical and simulated results demonstrate that the proposed approach is effective and feasible. Such results can help decision makers perform better judgments when a practical automotive service en
The optical switch based on the dimer plasmonic nano-tubes on the silicon waveguide has been numerically analysed. In the proposed switch, the optical switch has been excited by two monochromatic incident plan-waves w...
详细信息
The optical switch based on the dimer plasmonic nano-tubes on the silicon waveguide has been numerically analysed. In the proposed switch, the optical switch has been excited by two monochromatic incident plan-waves with the same frequency and two polar angles "theta = 0" and "theta = pi/2". If the signal with theta = 0 applies, the incident wave will be transmitted, and on the other hand coherent perfect absorption (CPA) will occur and the two incident waves will be suppressed, if we apply both signals to the switch at the same time. Therefore, the signal with theta = pi/2 acts as control signal. Since the CPA efficiency depends strongly on the number of plasmonic nano-tubes and its location, a new efficient binary optimization method based on the Teaching-Learning-Based optimization (TLBO) algorithm is proposed to design an optimized array of the plasmonic nano-tubes in order to achieve the maximum absorption coefficient in the 'off' state and the minimum absorption coefficient in the 'on' state. In Binary TLBO, a group of learners consists of a matrix with binary entries, control the presence ('1') or the absence ('0') of nano tubes in the array.
Trajectory optimization has been identified as an important way to reduce flight costs and polluting emissions. Due to the power capacity limitations in airborne devices such as the flight management system, a fast me...
详细信息
Trajectory optimization has been identified as an important way to reduce flight costs and polluting emissions. Due to the power capacity limitations in airborne devices such as the flight management system, a fast method should be implemented to calculate the full trajectory cost. Many flight management systems use a set of lookup tables with experimental data for each flight phase, and they are called performance databases. In this paper, the trajectory flight cost is calculated using a performance database instead of using classical equations of motion. The trajectory to be calculated is composed of climb, acceleration, cruise, descent, and deceleration. The influence of the crossover altitude during climb and descent, as well as step climbs in cruise, was considered. Lagrange linear interpolations were performed within the performance database discrete values to calculate the required values. By providing a takeoff weight, the initial and final coordinates, and the desired flight plan, the trajectory model provides the top-of-climb coordinates, the top-of-descent coordinates, the fuel burned, and the flight time needed to follow the given flight plan. The accuracy of the trajectory costs calculated with the proposed method was validated with an aerodynamic model in FlightSIM (R), which is software developed by Presagis (R), and with the trajectory cost given by the flight management system benchmark of reference. Results showed that, for the same reference trajectories and for the same inputs, the cost computed by the method proposed in this paper is close to the costs provided by FlightSIM and by the flight management system benchmark or reference.
Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing cer...
详细信息
Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.
Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum t...
详细信息
Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm. Note to Practitioners-This work concerns the uncertainty involved in facility location allocation (FLA). To deal with such uncertainty, it proposes a novel stochastic tradeoff method to solve an FLA problem. The prior research handles such a problem through a methodology based on the stochastic profit or cost, which is ineffective without considering the balance between the obtained profit of investors and the transportation cost of target customers. The goal of this work is to establish the tradeoff FLA model, i.e., to obtain assuredly the specific profit of investors, while minimizing the transportation cost of the service costumers for FLA. Both theoretical and simulat
Two methods for deriving first-order partial derivatives of the outputs with respect to the inputs of the Lambert boundary value problem are presented. The first method assumes the Lambert problem is solved via the un...
详细信息
Two methods for deriving first-order partial derivatives of the outputs with respect to the inputs of the Lambert boundary value problem are presented. The first method assumes the Lambert problem is solved via the universal vercosine formulation. Taking advantage of inherent symmetries and intermediate variables, the derivatives are expressed in a computationally efficient form. The typical added cost of computing these partials is found to be approximate to 15 to 35% of the Lambert computed cost. A second set of the same partial derivatives is derived from the fundamental perturbation matrix, also known as the state transition matrix of the Keplerian initial value problem. The equations are formulated in terms of Battin's partitions of the state transition matrix and its adjoint. This alternative approach works with any Lambert formulation, including one that solves a perturbed Lambert problem, subject to the availability of the associated state transition matrix. The analytic partial derivatives enable fast trajectory optimization formulations that implicitly enforce continuity constraints via embedded Lambert problems.
Currently, CCHP systems are being used over a wide area as a key alternative for producing power, heat and refrigeration. Here, a sample residential building in Mashhad city (Iran) has been selected as a case study to...
详细信息
Currently, CCHP systems are being used over a wide area as a key alternative for producing power, heat and refrigeration. Here, a sample residential building in Mashhad city (Iran) has been selected as a case study to investigate feasibility of employing CCHP systems to meet the energy demands for various buildings sizes. An optimization algorithm is developed to find the best operation point of the Power Generation Unit (PGU) at minimum energy cost. The algorithm optimizes the operation of the CCHP systems at first step. The results of the algorithm implementation for different PGU capacities and various buildings sizes, demonstrate the performance of the operationally optimized CCHP systems at second step. The results show that CCHP system has higher performance comparing to the separate production system considering different evaluation parameters. In small buildings, primary energy saving ratio is positive for PGU capacities between 1 kW and 15 kW, with a maximum amount of 17.24%. But in large buildings, it is positive for PGU capacities between 1 kW and 30 kW, with a maximum amount of 5.1%. The PGU with capacity of 350 kW in 30-units building by 51.18% has the highest amount of energy cost saving ratio. The CCHP system with 120 kW PGU capacity in 30-units building has the least simple payback ratio by 5.08 years. Considering each parameter individually, it is showed that there are some useful mathematical relationships between optimum PGU capacity and building size. Also it is found that there is an equation for best optimum PGU capacity, considering all of evaluation criteria simultaneously. (C) 2015 Elsevier Ltd. All rights reserved.
An efficient binary optimization method named binary particle swarm optimization (BPSO) algorithm was used to optimize an array of plasmonic nano-rods in order to design an optical switch. In the proposed switch, the ...
详细信息
An efficient binary optimization method named binary particle swarm optimization (BPSO) algorithm was used to optimize an array of plasmonic nano-rods in order to design an optical switch. In the proposed switch, the optical switch has been excited by two monochromatic incident plan-waves with the same frequency and two angles of incident theta = 0 and theta = 90. When only the signal with theta = 0 is applied, the incident wave is transmitted and when both signals are applied to the switch simultaneously, the coherent perfect absorption occurs and the two incident waves are suppressed. Therefore, the signal with theta = 90 acts as control signal. BPSO, a swarm of birds including a matrix with binary entries responsible for controlling nano-rods in the array, shows the presence with symbol of ('1') and the absence with ('0').
In this paper, different optimal hybrid techniques have been proposed for management of a hybrid power generation system including photovoltaic (PV),. fuel cell and battery. The main power of the hybrid system comes f...
详细信息
In this paper, different optimal hybrid techniques have been proposed for management of a hybrid power generation system including photovoltaic (PV),. fuel cell and battery. The main power of the hybrid system comes from the photovoltaic panels, while the fuel cell and batteries are used as back up units. In order to achieve maximum power point tracking for the photovoltaic system, both fuzzy logic controller and perturb and observation methods are examined and their performances have been investigated via simulations. Next, the performance of the hybrid system has been improved via employing a family of well-known optimization approaches for load sharing among the available resources. Imperialist Competitive algorithm (ICA), Particle Swarm optimization (PSO), Quantum behaved Particle Swarm optimization (QPSO), Ant Colony optimization (ACO), and Cuckoo optimization algorithm (COA) are used to manage the load sharing to achieve optimal performance while the system constraints are met. The optimal performance has been characterized via the control strategy performance measure being the ratio of the amount of hydrogen production with respect to the hydrogen consumption. In order to verify the system performance, simulation studies have been carried out using practical load demand data and real weather data (solar irradiance and air temperature). Different combination of maximum power point tracking methods with various optimization algorithms have been compared with each other. The results show that the combination of fuzzy logic controller with QPSO has the best performance among the considered combinations. In this situation, when the solar irradiation is noticeably high, the required load is supplied mainly by PV array, while the battery is charged, simultaneously. In the other times, the load is mainly fed by the battery and fuel cell while the performance constraints of battery is met and the daily performance measure is optimized. (C) 2014 Elsevier Ltd. All right
暂无评论