A Hybrid Metaheuristic Algorithm for Truss Structure Domain’s Optimization Problem

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.

To avoid the premature convergence caused by basic particle swarm optimization(PSO) in resolving engineering optimization design of highly complex and nonlinear constraints, a new particle swarm optimization algorithm with adaptive inertia weight (AIW-PSO) is proposed. In this algorithm, inertia weight is adaptively changed according to the current evolution speed and aggregation degree of the swarm, which provides the algorithm with dynamic adaptability, enhances the search ability and convergence performance of the algorithm. Moreover, penalty function is used to eliminate the constraints. Finally, the validity of AIW-PSO is verified through an optimization example.

Solving complex problems with higher dimensions involving many constraints is often a very challenging task. While solving multidimensional problems with particle swarm optimization involving several constraint factors, the penalty function approach is widely used. This paper provides a comprehensive survey of some of the frequently used constraint handling techniques currently used with particle swarm optimization. In this paper some of the penalty functional approaches for solving evolutionary algorithms are discussed and a comparative study is being performed with respect to various benchmark problems to assess their performance.

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.

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.

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.

The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain. With more importance being attached to PSO, the application of PSO algorithm to a great deal of different scientific fields have been introduced in many literature. Here, a PSO algorithm is applied to multilevel lot-sizing (MLLS) problems with series structure aiming at extending the research scope of PSO algorithm. Experiments showed the feasibility and effectiveness of PSO algorithm for solving this kind of MLLS problems.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

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.

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.

In this paper, we improved the particle swarm optimization (PSO) algorithm with inertial weight by adopting the sigmoid function. The IPSO is applied to structural damage identification in frame structures. Firstly the theoretical background of the improved PSO (IPSO) algorithm is presented. Secondly, we use a suite of four famous benchmark functions to evaluate the algorithm performance. Some comparative studies on single and multi-damage of a 2-story rigid frame structure have been carried out. The results of the numerical simulation demonstrate that the approach can detect the structural damage with higher accuracy.

Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4° of experimental kinematics with residual forces and moments of less than 10 N and 18 Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.

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

Considering that the global navigation satellite system (GNSS) has the influence of positioning and atmospheric signals from time to time in meteorology, errors caused by moisture, and so on in the effect of the propagation path, these factors have led to the influence of various indexes of meteorological factors. In this study, a meteorological prediction algorithm based on the CNSS and particle swarm optimization is proposed. Aiming at the phenomenon that the particle swarm optimization (PSO) algorithm is prone to slow convergence speed and low optimization accuracy and there is a local optimal but cannot achieve the global optimal, an adaptive Kent chaotic map PSO algorithm is proposed. Through the comprehensive analysis of the meteorological input indicators in the GNSS, a noncurrent weight evaluation system is proposed. Under different evaluation systems, the PSO algorithm is applied, and PCA weight can obtain the best prediction effect. Then, the GA model, PSO model, and ADPSO model are used to predict PM2.5 index in meteorology. The results show that the proposed ADPSO algorithm has a good performance in RMSE, MAE, and R2 model evaluation.