Particle Swarm Optimization (PSO) algorithm for wind farm optimal design

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692---2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem.

In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.

An improved chaos particle swarm optimization (CPSO) algorithm is proposed on path planning for unmanned aerial vehicle (UAV) to overcome the inadequacy of particle swarm optimization (PSO) algorithm, which falls into local optimum easily and converges slowly in process with poor precision. Through the in-depth analysis of PSO algorithm, the chaos optimization (CO) algorithm principle is introduced into it based on the traditional update operations on the particles’ velocity and position; as a result, the diversity of particles is increased, the suboptimal search on path planning is avoided and the quickness accompanied with accuracy of convergence is improved. Combined with digital map for modeling the UAV’s flight environment, the 3-D path planning is achieved. As the simulation results demonstrated, this hybrid algorithm is superior to the traditional PSO algorithm on path searching, especially in the 3-D environment.

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(PSO) algorithms, two new particle’s position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified PSO algorithms (called Leading PSO algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified PSO algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

To improve the simulation accuracy of disease prediction model, a modified hybrid algorithm combining BP neural network (BPNN) with particle swarm optimization (PSO) algorithm based on chaos theory optimization is proposed considering BPNN is easy to fall into the local extremum. The chaos theory is used to optimize PSO algorithm to overcome the premature convergence of the traditional PSO algorithm. Then, the improved CAPSO algorithm is used to train the BPNN, to make full use of the global search characteristic of PSO algorithm and the local search ability of BPNN. The fitness function of the PSO algorithm is used as the energy function, and the optimization method of the improved hybrid algorithm is selected according to the specified number of evolution. An optimized network model was used to predict the prevalence of coronary heart disease. Compared with other algorithms such as BP neural network, the results show that the proposed algorithm has high accuracy and can significantly improve the quality of prediction.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

The overloaded CDMA system, in which the number of users is larger than the dimension of signal space experiences a very serious problem of multiple access interference which is the major limiting factor for the channel capacity. In this letter we used multicarrier communication to resolve the problem of overloading. Computational complexity associated with optimum maximum likelihood detector (MLD) in wideband code division multiple access (WCDMA) systems is also addressed by using the particle swarm optimization (PSO) algorithm. In the proposed scheme, large number of users in an overloaded system is divided in groups. A carrier is modulated with the composite signal of each group. The carriers of each group are orthogonal to each other. Detection of individual users in a particular group is accomplished through PSO algorithm. PSO algorithm lowers the search space compared to MLD. Simulation results show that PSO algorithm avoids being trapped in any local minima and gives a much faster convergence compared to MLD. It also performed better than other suboptimal detectors.

This paper presents the use of particle swarm optimization (PSO) algorithm applied to the optimal design of the 4/2 switched reluctance motor (SRM). The main advantage of designing 4/2 SRM is the robust rotor structure for high speed unidirectional rotating applications such as the air conditioner's blower. In the designing process, the nite element method magnetics (FEMM) is employed to analyze the designing SRM with its optimized parameters given by PSO. This PSO algorithm is ecient and exible. The objective function is based on the ripple torque minimization with respect to rotor node position. The PSO algorithm is described and the FEMM simulation results with detailed analysis will be given for verifying optimal rotor design by PSO algorithm.

This paper presents the use of particle swarm optimization (PSO) algorithm applied to the optimal design of the 4/2 switched reluctance motor (SRM). The main advantage of designing 4/2 SRM is the robust rotor structure for high speed unidirectional rotating applications such as the air conditioner's blower. In the designing process, the finite element method magnetics (FEMM) is also employed to analyze the designing SRM with its optimized parameters given by PSO. This PSO algorithm is efficient and flexible. The objective function is based on the ripple torque minimization with respect to rotor node position. The PSO algorithm is described and the preliminary FEMM simulation results will be given for verifying optimal rotor design by PSO algorithm. In the final paper, the complete FEMM simulation results with detailed analysis will be provided.

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

The wireless mesh network is a new emerging technology exploiting the advantages of multiple radios and multiple channels. In this paper we have studied the channel assignment problem which is to assign channels to links in the network, and aimed at minimizing the network interference so as to improve the network throughput. The channel assignment problem is proved to be NP-hard. In this paper, we have developed a new approach based on the particle swarm optimization (PSO) algorithm and a heuristic algorithm to solve channel assignment problem in multi-radio multi-channel wireless mesh networks. Meanwhile, the crossover operation is employed in the discrete particle swarm optimization (DPSO) algorithm to handle the discrete channel assignment problem. Experimental results have shown that the rate of interference in the DPSO algorithm descends by 67.67% compared with the Tabu-based algorithm when there are 12 channels and 5 radios. And the heuristic algorithm can also yield good solutions with good quality in both dense and sparse networks.

Injection molding (IM) is a rapid process that injects molten plastic into a mold cavity for cooling and solidification. It is widely used in daily necessities, auto parts, medical equipment, electronic products, and infant toys. However, the process parameters of IM are complex and interact with each other. IM simulation software can not only provide optimized solutions to process problems, but also improve the quality of manufactured parts. This study report combines Taguchi design and computational software (Moldflow, MINITAB, and MATLAB) to analyze and simulate the process parameters of the back cover of the LCD monitor to obtain its minimum warpage (W) in z-direction. The results of Taguchi design (traditional optimization technique) show that the optimal processing parameter combination to obtain W is 40°C of mold temperature (A1), 220°C of plastic temperature (B1), 70 MPa of packing pressure (C1), 5 s of holding time (D3), 20 s of cooling time (E1), 60 cm3/s of injection speed (F1), 40% of switch position (G1), and 90 MPa of the injection pressure (H3), named as A1B1C1D3E1F1G1H3. Four important parameters (B, C, D, and G) were obtained by Taguchi design in IM process. The computed warpage errors, obtained by Moldflow software and regression analysis, are less than 2.5%. However, Taguchi design can only result in an optimal set of combinations of specified processing parameters. The particle swarm optimization (PSO) algorithm, which is a non-traditional optimization technique, often overcomes obstacles faced by Taguchi design. The examined warpage errors through Moldflow software and PSO are less than 3.5%. It is shown that the combination of Taguchi design and PSO algorithm is very suitable for processing optimization of IM production. Its smart manufacturing method can not only reduce production costs and time, but also increase the flexibility of processing parameter selection to a greater extent.