Optimal Sizing of Microgrid System Using Hybrid Firefly and Particle Swarm Optimization Algorithm

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

In this study, a new design approach using HFPSO algorithm, which is obtained by hybridization of firefly algorithm (FA) with particle swarm optimization (PSO) algorithm is presented for the adjustment of proportional-integral-derivative (PID) controller parameters in a buck converter. Using the proposed approach, it is aimed to improve the stability profile of the relevant system. In order to demonstrate the superiority of the proposed HFPSO-based PID (HFPSO/PID) controller, stability analyzes in time domain and frequency domain, and disturbance suppression analysis were performed comparatively. The results obtained from analyzes showed that the proposed HFPSO/PID controller is more efficient than the whale optimization algorithm (WOA) based PID (WOA/PID) controller and genetic algorithm (GA) based PID (GA/PID) controller to improve the control performance of the buck converter.

A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

The impact of local search strategies on chaotic hybrid firefly particle swarm optimization algorithm in flow-shop scheduling

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

In this paper, we present a Hybrid Particle Swarm Optimization (HPSO) algorithm that combines the Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm (GA) method. The algorithm is used for investigating the plastic deformation behavior of titanium alloy (Ti-6Al-4V) in a multi-hole extrusion process. The simulation used rigid-plastic finite element (FE) DEFORMTM-3D software to obtain the minimum mandrel bias angle and exit tube bending angle. Results of the simulation indicate that these two angles were significantly less than 0.3 degrees, suggesting that metaheuristic algorithms based on HPSO and FE analysis could be used for general multi-hole extrusion processes.KeywordsMulti-hole extrusionParticle Swarm OptimizationFinite elementGenetic Algorithm

A new hybrid particle swarm optimization (HPSO) algorithm which is based on particle swarm optimization algorithm and simulated annealing algorithm is proposed in this paper for solving multiple capacitated vehicle routing problem. The basic scheme consists in Particle swarm optimization with time-varying parameters and simulated annealing with memory and tempering features. Results from the computational experiment shows that the hybrid particle swarm algorithm is effective.

This paper introduces a new algorithmic nature inspired approach that uses a hybridized Particle Swarm Optimization algorithm with a new neighborhood topology for successfully solving the Feature Selection Problem (FSP). The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. The proposed algorithm for the solution of the FSP, the Particle Swarm Optimization with Expanding Neighborhood Topology (PSOENT), combines a Particle Swarm Optimization (PSO) algorithm and the Variable Neighborhood Search (VNS) strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method by using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) the size of the neighborhood, it ends to a neighborhood that includes all the swarm, it manages to take advantage of the exploration capabilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method we use data sets of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Novel Artificial Human Optimization Field Algorithms - The Beginning

A large project in a company is often divided to several subtasks, which would be assigned to different people with variant abilities to the same task. So whether the tasks are scheduled properly would determine the quality or the efficiency of team collaboration. A hybrid particle swarm optimization (PSO) algorithm is putted forward. Subtasks are disassembled from the project by using the task tree relations, and the tree structure is modeled into a task matrix. Moreover, task-time matrix is used to indicate the people abilities to complete the tasks. Then the hybrid algorithm was presented, in which simulated annealing method is added in particle swarm optimization to improve the capability of seeking the best allocating results. Finally, a simulation experiment is carried out by using the proposed algorithm, the comparing results show that the convergent velocity is fast and the optimizing ability is preferable.

Network-on-chip (NoC) has been proposed as a solution for the global communication challenges of system-on-chip (SoC) design in the nanoscale technologies. In this paper, a methodology is presented to develop an efficient routing algorithm for network-on-chip platforms that are specialized for an application or a set of concurrent applications. The proposed routing methodology, based on the hybrid particle swarm optimization (PSO) Algorithm, is applied on the 2D-mesh NoC platform to balance the link load. Experimental results show that this routing algorithm can efficiently assign deterministic, deadlock-free, minimal routing paths for traffic traces in a short period of time, and significantly guarantee the bandwidth requirement. In addition, the Hybrid PSO is combined with the operations of GA Algorithm, so that the algorithm achieves better performance.