Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.

Research on Game Operation of Multi-Stakeholder in Distribution Network in Electricity Market

With the development of the electricity market, various stakeholders such as batteries, multi-microgrid (MMG), and electric vehicle (EV) clusters, can trade with either the distribution network or each other to meet their power balance needs and to maximize their profits. This paper proposes a two-level game model based on game theory to study the operation strategy of stakeholders in the distribution network. First, each stakeholder predicts its electricity demand profile. A Markov Decision Process (MDP) model of random variables is established to predict the charging and discharging power of the battery. Then, the two-level game is presented to let multi-stakeholder participate, in which different kinds of stakeholders have different game strategy limits. Additionally, suggestions for battery operation modes under different compensation coefficients are given to participate in the subsequent two-level game. An algorithm is proposed to allow stakeholders to merge or split self-adaptively based on Nondominated Sorting Genetic Algorithm II (NSGA-II) to optimize operation mode. Finally, the proposed model is applied to the PG and E69-bus distribution system and a practical 101-bus distribution system in China. The case studies show that different game strategy limits of the stakeholders will affect the distribution of the Nash equilibrium (NE) solutions. The multi-stakeholder system can better absorb regional unbalanced power through electricity transactions, and further increase the benefits of each stakeholder.

Validation and Optimization of Suspension Design for Testing Platform Vehicle

With the application and popularization of the advanced driving assistance system (ADAS), the reliability and stability of ADAS have become its research focus. This article presents a car testing framework for ADAS reliability and stability. Its special suspension has been designed, verified, and optimized in real vehicles according to its working conditions. First, the structure and working principle of the testing platform vehicle are introduced. Then a simulation model is built in MATLAB/Simulink based on the dynamic equation to verify the working characteristics of the suspension. Experimental vehicle tests are conducted for simulation verification purposes. During the analysis, the root-mean-square (RMS) values of vehicle body displacement and dynamic tire deflection are considered evaluation indices. The nondominated sorting genetic algorithm (NSGA-II) is used to optimize the damping, stiffness, and installation position of the suspension system. The findings demonstrate that the specially designed suspension in this article can fulfill the test criteria. Compared with the optimized suspension performance, both the vehicle body displacement and dynamic tire deflection have decreased roughly by 17 and 40%, respectively, which significantly improves the suspension performance and provides a reference for the future designs of testing platform vehicles.

AI-Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state-of-arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

Multi-Depot Pickup and Delivery Problem with Resource Sharing

Resource sharing (RS) integrated into the optimization of multi-depot pickup and delivery problem (MDPDP) can greatly reduce the logistics operating cost and required transportation resources by reconfiguring the logistics network. This study formulates and solves an MDPDP with RS (MDPDPRS). First, a bi-objective mathematical programming model that minimizes the logistics cost and the number of vehicles is constructed, in which vehicles are allowed to be used multiple times by one or multiple logistics facilities. Second, a two-stage hybrid algorithm composed of a k-means clustering algorithm, a Clark-Wright (CW) algorithm, and a nondominated sorting genetic algorithm II (NSGA-II) is designed. The k-means algorithm is adopted in the first stage to reallocate customers to logistics facilities according to the Manhattan distance between them, by which the computational complexity of solving the MDPDPRS is reduced. In the second stage, CW and NSGA-II are adopted jointly to optimize the vehicle routes and find the Pareto optimal solutions. CW algorithm is used to select the initial solution, which can increase the speed of finding the optimal solution during NSGA-II. Fast nondominated sorting operator and elite strategy selection operator are utilized to maintain the diversity of solutions in NSGA-II. Third, benchmark tests are conducted to verify the performance and effectiveness of the proposed two-stage hybrid algorithm, and numerical results prove that the proposed methodology outperforms the standard NSGA-II and multi-objective particle swarm optimization algorithm. Finally, optimization results of a real-world logistics network from Chongqing confirm the applicability of the mathematical model and the designed solution algorithm. Solving the MDPDPRS provides a management tool for logistics enterprises to improve resource configuration and optimize logistics operation efficiency.