Designing a fuzzy logic controller with particle swarm optimisation algorithm

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.

A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.

This paper presents novel structures for optimization and communication of a swarm of mobile sensors or robots for maximizing local and global tasks such as firefighting, landmine detection, radioactivity detection, etc. The navigation of the sensors is carried out using two strategies. The first strategy is based on particle swarm optimization (PSO) and the second strategy is based on a swarm of fuzzy logic based controllers. In addition, the membership functions and the rules of the fuzzy logic controller (FLC) are optimized using the PSO algorithm. Navigation of mobile sensors is considered in this paper to locate desirable target sources in a given sensing area. Both approaches presented do not depend on the number of target sources. Results are provided for target locations based on a PSO, a swarm of fuzzy logic controllers and a swarm of optimized fuzzy logic controllers.

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.

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

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.

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.

A fuzzy logic controller (FLC) is designed to achieve course-keeping for mooring shifting system, which is the main system of non self-propelled vessels. Compared with manual operation, the automatic operation and monitoring system with the FLC can perform higher precision and efficiency. The particle swarm optimization (PSO) algorithm is introduced to optimize the proposed FLCpsilas parameters. A series of simulation studies have been undertaken to compare the performance of a basis FLC and PSO based FLC. The results demonstrate that the latter has the better controlling quality.

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 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.

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.

This paper presents a particle swarm optimization algorithm (PSO) as a control strategy for the offline driving cycle to obtain the best torque distribution between the two sources: internal combustion engine (ICE) and the electric motor (EM). The purpose to minimize the fuel consumption, emissions, and maximize the state of charge of the battery for the model of power-split hybrid electric vehicles (PSHEV), while the requirements of the driving performance considered as constraints. The control strategy has been applied for the UDDS driving cycle under the Matlab Simulink software environment. The results of the value of fuel consumption compared with fuzzy logic control (FLC), the global optimization genetic algorithm (GA), and ADVISOR. After comparing, the results demonstrate the effectiveness of the (PSO) algorithm over the mentioned methods in lowering fuel consumption by 8.87% for the (FLC), 22.6% for the GA. Maximizing the state of charge of the battery by 5.6% for the ADVISOR program and closest to optimal results for FLC

The paper proposes a modified particle swarm optimization algorithm (MPSO) for online tuning to the fuzzy controller of power system stabilizer (PSS). The hybrid fuzzy controller combines concepts from fuzzy systems and particle swarm optimization to produce powerful system controllers that are used for adaptive control. Two synchronous generators fitted with a PSS and are connected via double transmission lines will be the system under study. Three types of PSS controllers such as Conventional lead-lag PSS (CPSS), Static Fuzzy Logic Controller (SFLC) and online tuning Fuzzy Logic Controller based on PSO algorithm will be considered. The modified PSO algorithm used to adjust the fuzzy set parameters of each Fuzzy Logic Controller (FLC-PSO) online. The speed error and the change of error are the input vector, and the stabilizing signal is the output of both fuzzy logic controller (FLC). The FLC controllers will be tested under different disturbances for a wide-range of operating points. The computation time and the complexity of the fuzzy controller are reduced. The simulation results ensure the effectiveness of the proposed FLC-PSO controller for achieving the good dynamic response of the system under study.