Hardware implementation of the particle swarm optimization algorithm

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.

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

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.

In this study, we designed a trajectory-tracking fuzzy logic controller (TTFLC) for the TRIGA Mark-II Training and Research Reactor, which is located at the Istanbul Technical University. The designed fuzzy logic controller (FLC) is based on the zeroorder Sugeno method. The parameters of the FLC membership functions and the action weights of the 15 rules in the rule base are optimised by using the particle swarm optimisation (PSO) algorithm. The objective of this study is to control the TRIGA Mark-II reactor using the designed PSO-tuning TTFLC in a simulator. We used a simulation code from the literature called `YAVCAN' for studying the non-linear behaviour of the core of the TRIGA Mark-II reactor. To select the best parameters of the PSO algorithm for this system, we conducted some experiments. After selecting the best PSO parameters, the algorithm was started a number of times to determine the optimal parameters of the designed FLC and the optimal parameters of the controller. After determining these parameters, the performance of the designed controller was tested for various initial and desired power levels and under conditions of disturbance. The simulation results showed that the proposed controller could control the reactor power successfully, and it could ensure that the reactor tracks the desired trajectory power within the acceptable error tolerance. Therefore, the PSO algorithm is suitable for finding the optimal parameters of the FLC.

The learning algorithm of fuzzy cognitive map(FCM) is mainly based on expert intervention or effective historical data to learn the weight value in weight matrix W. With the increase of the number of nodes, the number of weights need to be learned will also increase square, the performance of the learning algorithm will decline sharply. Therefore, it is an important task to design a high-performance learning algorithm which depends on expert knowledge as little as possible. A single learning algorithm has some limitations, such as easy local optimization, slow learning rate and so on. Among them, particle swarm optimization algorithm has strong global optimization ability, which can quickly optimize the weight matrix to a certain accuracy, but the follow-up optimization time is too long. For the gradient descent algorithm, when the initial value is chosen properly, its can approach the global optimal solution quickly. In this paper, we design particle swarm optimization (PSO) and gradient descent algorithm (GDA) to learn FCM respectively. According to the characteristics of the two algorithms, a hybrid algorithm(HA) is proposed: first, the PSO algorithm is used to optimize weight matrix to a certain accuracy, then the gradient descent method is added, which makes up for the disadvantage that the error of single PSO algorithm decreases slowly in the later stage. Experimental results show that the hybrid algorithm proposed in this paper has a significant improvement in both accuracy and speed.

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.

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.

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.

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.

In this work we consider the problem of Hidden Markov Models (HMM) training. This problem can be considered as a global optimization problem and we focus our study on the Particle Swarm Optimization (PSO) algorithm. To take advantage of the search strategy adopted by PSO, we need to modify the HMM's search space. Moreover, we introduce a local search technique from the field of HMMs and that is known as the Baum–Welch algorithm. A parameter study is then presented to evaluate the importance of several parameters of PSO on artificial data and natural data extracted from images.

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.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

In this paper, three new particle swarm optimization (PSO) algorithms are compared with the state of the art PSO algorithms for the optimal steady-state performance of power systems, namely, the reactive power and voltage control. Two of the three introduced, the enhanced GPAC PSO and LPAC PSO, are based on the global and local-neighborhood variant PSOs, respectively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of passive congregation observed in swarms. The third one is based on a new concept of coordinated aggregation (CA) and simulates how the achievements of particles can be distributed in the swarm affecting its manipulation. Specifically, each particle in the swarm is attracted only by particles with better achievements than its own, with the exception of the particle with the best achievement, which moves randomly as a crazy agent. The obtained results by the enhanced general passive congregation (GPAC), local passive congregation (LPAC), and CA on the IEEE 30-bus and IEEE 118-bus systems are compared with an interior point (IP)-based OPF algorithm, a conventional PSO algorithm, and an evolutionary algorithm (EA), demonstrating the excellent performance of the proposed PSO algorithms