Multi-path routing algorithm in WSN using an improvedparticle swarm optimization

To slove the problems of constrained energy and unbalanced load of wireless sensor network(WSN)nodes,a multipath load balancing routing algorithm based on neighborhood subspace cooperation is proposed.The algorithm adopts the improved particle swarm optimization(PSO)algorithm,takes the shortest distance and minimum energy consumption as optimization target and divides the nodes in one-hop neighborhood near the base station area into different regions.Furthermore,the algorithm designs a fitness function to find the best node in each region as a relay node and forward the data in parallel through the different paths of the relay nodes.The simulation results show that the proposed algorithm can reduce energy consumption and average end-to-end delay,balance network load and prolong network lifetime effectively.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

Multi-objective reconfigurable production line scheduling for smart home appliances

In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.

Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm

Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.