Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P-V characteristic with multiple local maximum power points, which makes the existing maximum power point ...tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.
There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we ...propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.
Mathematical modeling plays an important role in biology for describing the dynamics of infectious diseases. A useful strategy for controlling infections and disorder conditions is to adopt ...computational algorithms for determining interactions among their processes. The use of fractional order (FO) calculus has been proposed as one relevant tool for improving heuristic models. The particles memory is captured by the FO derivative and that strategy opens the door for grasping the memory of the long-term particle past behavior. This papers studies the analytical convergence of FO particle swarm optimization algorithm (FOPSO) based on a weak stagnation assumption. This approach allows establishing systematic guidelines for the FOPSO parameters tuning. The FOPSO is formulated on the basis of a control block diagram and the particle dynamics are represented as a nonlinear feedback. To describe the historical evolution of the particles, a state-space representation of different types of the FOPSO is formulated as a delayed discrete-time system. The existence and uniqueness of the equilibrium point of the FOPSO are discussed, and the stability analysis is derived to determine its convergence boundaries. Several simulations confirm the stability region of the FOPSO equilibrium point. The algorithm is also applied to a practical application, namely the minimization of the blood glucose injection in Type I diabetes mellitus patients.
In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel ...inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching-learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.
Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted ...metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length representation, which ...is inflexible and limits the performance of PSO for FS. When applying these methods to high-dimensional data, it not only consumes a significant amount of memory but also requires a high computational cost. Overcoming this limitation enables PSO to work on data with much higher dimensionality which has become more and more popular with the advance of data collection technologies. In this paper, we propose the first variable-length PSO representation for FS, enabling particles to have different and shorter lengths, which defines smaller search space and therefore, improves the performance of PSO. By rearranging features in a descending order of their relevance, we facilitate particles with shorter lengths to achieve better classification performance. Furthermore, using the proposed length changing mechanism, PSO can jump out of local optima, further narrow the search space and focus its search on smaller and more fruitful area. These strategies enable PSO to reach better solutions in a shorter time. Results on ten high-dimensional datasets with varying difficulties show that the proposed variable-length PSO can achieve much smaller feature subsets with significantly higher classification performance in much shorter time than the fixed-length PSO methods. The proposed method also outperformed the compared non-PSO FS methods in most cases.
The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy ...(MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).
Summary
Currently, the ideal sizing of hybrid technologies is one of the vital aspects of power system design. In this article, the design and optimization of the sizing of hybrid renewable energy ...systems (HRESs) with power‐sharing capabilities in conjunction with electric vehicles (EVs) were proposed in two case studies. Two algorithms, namely, multi‐objective particle swarm optimization (MOPSO) and multi‐objective crow search (MOCS), have been formulated and were used to solve the problem being investigated. In case study 1 (CS1), four different HRESs are designed in the presence of EVs, meaning that for each HRES an EV and the power‐sharing capability is employed. And also, the stochastic behavior of the EV using Monte Carlo simulation (MCS) is modeled. In case study 2 (CS2), four HRESs are designed with power‐sharing capabilities, but in this case, for any of the HRESs, EV is not considered. This idea can be considered a novel breakthrough for the potential of power‐sharing has been incorporated with the integration of EVs and HRESs. This approach improves the life cycle cost and loss of power supply probability indices. In summary, both cases in the presence and absence of EVs were compared with the simulation results. The results show that the use of the proposed EV significantly reduces the total cost of the engineered system. Furthermore, two meta‐heuristic techniques were compared, and it was concluded that MOPSO had performed better than MOCS.
Highlights
Optimal sizing and power sharing of hybrid renewable systems with EVs.
Proposed novel heuristic optimization approach using MOPSO and MOCS.
Optimization of uncertainty parameters using 100 different scenarios using MCS.
Economic and reliability benefits of the proposed system.
Feature selection (FS) is an important preprocessing technique for improving the quality of feature sets in many practical applications. Particle swarm optimization (PSO) has been widely used for FS ...due to being efficient and easy to implement. However, when dealing with high-dimensional data, most of the existing PSO-based FS approaches face the problems of falling into local optima and high-computational cost. Evolutionary multitasking is an effective paradigm to enhance global search capability and accelerate convergence by knowledge transfer among related tasks. Inspired by evolutionary multitasking, this article proposes a multitasking PSO approach for high-dimensional FS. The approach converts a high-dimensional FS task into several related low-dimensional FS tasks, then finds an optimal feature subset by knowledge transfer between these low-dimensional FS tasks. Specifically, a novel task generation strategy based on the importance of features is developed, which can generate highly related tasks from a dataset adaptively. In addition, a new knowledge transfer mechanism is presented, which can effectively implement positive knowledge transfer among related tasks. The results demonstrate that the proposed method can evolve a feature subset with higher classification accuracy in a shorter time than other state-of-the-art FS methods on high-dimensional classification.
•We developed new hybrid evolutionary algorithm for solving generator maintenance scheduling problem.•Hybrid optimization method balance overall reliability and economy.•A case study of 32 thermal ...generating units reveal the effectiveness of the hybrid method.
This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.
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