A Particle Swarm Optimization Approach to A Multi-objective Reconfigurable Machine Tool 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.

Measuring the roundness of a circular workpiece is a crucial issue of quality control and inspection in industry. In this area, maximum inscribed circle (MIC) and Maximum circumscribing circle (MCC), Minimum zone circle (MZC) and Least Square Circle (LSC) are four commonly used methods. In particular, MIC, MCC, and MZC, which are non-linear constrained optimization problems, have not been thoroughly discussed lately. This study proposes a roundness measuring method that applies the Particle Swarm Optimization Algorithm (PSO) to compute MIC, MCC and MZC. To facilitate the PSO process, five different PSO methods are encoded using a radius (R) and circle center (x, y) and extensively evaluated using an experimental design, in which the impact of inertia weight, maximum velocity and the number of particles on the performance of the particle swarm optimizer is analyzed. The proposed method is verified with a set of testing images and benchmarked with the GA-based (Genetic Algorithm) method (Chen, 2000). The experimental results reveal that the PSO-based method effectively solved the MIC, MCC, and MZC problems and outperforms GA-based method in both accuracy and the efficiency. As a result, several industrial applications are presented to explore the effectiveness and efficiency of the proposed method.

In response to the need for cost-effective systems than can be quickly adapted to changes in product design and manufacturing processes, the next generation of machine tools should be reconfigurable and intelligent. Reconfigurability allows for the reduction of machine design lead time, machine set-up and ramp-up time. The principal characteristics of the Reconfigurable and Intelligent Machines are modularity, convertibility, flexibility and cost-effectiveness. This paper applies a concurrent design reference model to Reconfigurable Machine Tool development. A methodology for this purpose is introduced, and the bottlenecks in the process are identified, namely, the procedure to design modules. In response, a technique for the development of modules that are consistent with the product portfolio of the machine tool builder is outlined.KeywordsModularityReconfigurabilityConcurrent DesignMachine Tool

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.

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.

Reconfigurable Machines form a new class of machines that are designed around a specific part family of products and allow rapid change in their structure. They are designed to allow changes in production requirements by changes in the machine mechanical configuration and structure. Reconfigurable machines might be designed for various types of production operations such as machining, inspection and assembly. This paper introduces characteristics and design principles of reconfigurable machines, and describes their features using an example of our new full-scale industrial prototype of a Reconfigurable Bore Surface Inspection Machine (RBSIM). In addition, the paper also describes briefly other three prototypes of Reconfigurable Machines that were developed during recent years in our center: (1) the Reconfigurable Machine Tool (RMT), (2) the Reconfigurable Inspection Machine (RIM), and (3) the Reconfigurable Assembly Machine (RAM).

Particle swarm optimization approach for multi-objective composite box-beam design

Cable-driven parallel robots (CDPRs) have been widely used in engineering fields because of their significant advantages including high load-bearing capacity, large workspace, and low inertia. However, the impact of convergence speed and solution accuracy of optimization approaches on optimal performances can become a key issue when it comes to the optimal design of CDPR applied to large storage space. An adaptive adjustment inertia weight particle swarm optimization (AAIWPSO) algorithm is proposed for the multi-objective optimal design of CDPR. The kinematic and static models of CDPR are established based on the principle of virtual work. Subsequently, two performance indices including workspace and dexterity are derived. A multi-objective optimization model is established based on performance indices. The AAIWPSO algorithm introduces an adaptive adjustment inertia weight to improve the convergence efficiency and accuracy of traditional particle swarm optimization (PSO) algorithm. Numerical examples demonstrate that final convergence values of the objective function by the AAIWPSO algorithm can almost be 14∼20% and 19∼40% higher than those by the PSO algorithm and genetic algorithm (GA) for the optimal design of CDPR with different configurations and masses of end-effectors, respectively.

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.

Reconfigurable manufacture system is to list as one of key technologies which will first solve in the future manufacturing, this article will mainly solve research on theory of reconfigurable for NC machine tool. Reconfigurable machine tool has many merits that include modularization, integration, custom-made, convertibility and so on. That is the traditional machine tool cannot compare.This article has carried on the analysis from the deep hole components classification and the formed movement craft two aspects to the deep hole components race. The reconfigurable deep hole machine tool was to carry on the modular design based two aspects for the module partition and reconstructable analyze, solved the key technologies the machine tool modulation design. From the machine tool bed module, the head of machine tool module, the cutting tool auxiliary system and the numerical control system four aspects, to design and research on reconfigurable machine tool, solved the difficult design problem which the part reconfigurable designed. Finally uses the ultrasonic wave examination technology, has realized numerical control machine tool element level restructuring. (5 pages)

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines’ makespan and minimising jobs’ tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.

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.

Keeping networks security has never been such an imperative task as today. In this paper, by combining particle swarm optimization and clustering algorithm, a new detection method, Intrusion Detection based on Unsupervised Clustering and PSO algorithm (IDCPSO), is proposed. Particle swarm optimization algorithm is used to optimize the clustering results and obtain the optimal detection result. IDCPSO needs unlabeled data for training and automatically establish clusters and detect intruders by labeling normal and abnormal groups. Computer experiment results with KDD cup show that this algorithm is effective for intrusion detection.