A distributed co-evolutionary particle swarm optimization algorithm

Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm

The revolution represented by third-generation photovoltaic devices relied on the discovery of various hybrid organic–inorganic perovskite materials to convert solar into electrical energy. One of the advantages of such cells is their low cost due to the raw materials and cheap production methods used. Nevertheless, these cells face several challenges, such as inadequate stability and the hysteresis phenomenon. To overcome these, perovskite solar cell (PSCs) with planar and inverted structures have been utilized with an inorganic hole transport layer (HTL), achieving acceptable efficiency. As there is no closed-form system of equations to describe the operation of such cells, neural networks have been employed for their modeling. In optimization algorithms, the values of the parameters must be swept, since most current simulation tools cannot use them directly. Such software optimization can notably decrease the cost of cell design. This paper presents a practical way to achieve the mentioned aim. In particular, an artificial neural network (ANN) is exploited for the modeling, then an evolutionary particle swarm optimization (E-PSO) algorithm is developed to optimize the structure to achieve the highest efficiency based on searching the energy conversion. The results of the simulations are then employed in SCAPS software to train the neural network. This optimization leads to the achievement of an efficiency of 23.76% for the proposed structure, better than values reported in literature.

Various optimization methods are used along with the standard clustering algorithms to make the clustering process simpler and quicker. In this paper we propose a new hybrid technique of clustering known as K-Evolutionary Particle Swarm Optimization (KEPSO) based on the concept of Particle Swarm Optimization (PSO). The proposed algorithm uses the K-means algorithm as the first step and the Evolutionary Particle Swarm Optimization (EPSO) algorithm as the second step to perform clustering. The experiments were performed using the clustering benchmark data. This method was compared with the standard K-means and EPSO algorithms. The results show that this method produced compact results and performed faster than other clustering algorithms. Later, the algorithm was used to cluster web pages. The web pages were clustered by first cleaning the unnecessary data and then labeling the obtained web pages to categorize them.

Particle Swarm Optimization (PSO), a meta-heuristic algorithm inspired by swarm intelligence, is widely applied to various optimization problems due to its simplicity, ease of implementation, and fast convergence. However, PSO frequently converges prematurely to local optima when addressing single-objective numerical optimization problems due to its inherent rapid convergence. To address this issue, we propose a hybrid differential evolution (DE) particle swarm optimization algorithm based on dynamic strategies (MDE-DPSO). In our proposed algorithm, we first introduce a novel dynamic inertia weight method along with adaptive acceleration coefficients to dynamically adjust the particles’ search range. Secondly, we propose a dynamic velocity update strategy that integrates the center nearest particle and a perturbation term. Finally, the mutation crossover operator of DE is applied to PSO, selecting the appropriate mutation strategy based on particle improvement, which generates a mutant vector. This vector is then combined with the current particle’s best position through crossover, aiding particles in escaping local optima. To validate the efficacy of MDE-DPSO, we evaluated it on the CEC2013, CEC2014, CEC2017, and CEC2022 benchmark suites, comparing its performance against fifteen algorithms. The experimental results indicate that our proposed algorithm demonstrates significant competitiveness.

This paper attempts to substitute Resource Leveling Problem (RLP) into multi-project environment and construct Resource Leveling Problem of Multi-project (RLPMP) model with the goal of minimizing the sum of weighted mean square deviations of multi-resource requirements. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist reservation (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of Particle Swarm Optimization (PSO) algorithm with dynamic inertia weight. Through the dynamic change of inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can effectively solve the RLPMP model, and then effectively improve the balance of multi-project resources.

In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the “brain” of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution.

The increased integration of distributed energy resources (DER), based mainly on renewable sources, imposes new challenges on the distribution system operation and planning. The recent developments in energy storage technologies provide opportunities to use them as distributed storage systems. This paper presents a framework to assess the investments in battery energy storage systems (BESS) for distribution expansion planning, considering a high penetration of solar photovoltaic (PV) distributed generators. The size and allocation of BESS are optimized by applying the Evolutionary Particle Swarm Optimization (EPSO) algorithm and using the OpenDSS simulator to model and simulate the distribution network. The proposed framework is tested on a typical Latin American distribution network, considering first, conventional network reinforcements and later, taking into account BESS as an expansion option. The main results show the greatest contribution of BESS could lie in the flexibility provided to the distribution expansion planning by deferring capital-intensive investment network reinforcements.

The orthorectification of very high resolution (VHR) satellite images is a fundamental process to ensure the interoperability of the spatial information obtained from them. Therefore, previous studies have used different types of inputs, including multiple sources of control points and digital elevation models (DEM) of all kinds, in addition to testing different optimization methods. This research aims to jointly use and evaluate the use of the evolutionary algorithm Particle Swarm Optimization (PSO), stereoscopic points from photogrammetric blocks and DEM from different sources, to obtain cartographic products of scale 1:10.000 comparing their results with those obtained by ordinary least squares from previous experiments. The methodological proposal includes the entire procedure for the estimation and selection of the coefficients of the Rational Functional Model (RFM) spatial arrangement model using Particle Swarm Optimization and the Feature Condition Analysis method together with all the flow necessary for the generation of the final orthoimage. As a result, it is observed that the use of the PSO improves on average by 3% the positional accuracy of the orthoimage, however its use requires high computational resources and also this type of optimization method is not available yet in specialized software, being difficult for massive implementation in cartographic production processes.

Two-dimensional bin-packing problem with conflicts and load balancing: A hybrid chaotic and evolutionary particle swarm optimization algorithm

With the development of the Internet of Things and 5G. Edge cloud technology has gradually become a research hotspot. When facing the massive and concurrent tasks of terminal users, reasonable resource scheduling strategy is a key technology. Because edge cloud needs to respond quickly to real-time tasks and ensure the stability of nodes at the same time, the optimal task scheduling strategy needs to be selected to meet the low latency requirements of edge users. In view of the above problems in resource allocation of edge cloud, this paper established a layered excellent gene replication strategy (HEGPSO model), in which the optimal replicator is added, and an evolutionary particle swarm optimization algorithm is proposed. In each iteration, the population is divided into three layers based on individual fitness. After that, the optimal replication factor is added to each layer of individuals to enhance the global search ability of the algorithm and ensure the good convergence of the algorithm. Finally, a balanced resource allocation model is established. Experiments show that the HEGPSO model proposed in this paper has high fitness and fast convergence speed, and is suitable for large-scale task access scenarios.

This paper provides an algorithm for selecting the site and size of dispersed energy storage (DES), in power transmission networks. Firstly, flexibility requirements for each branch are identified through an assessment of the transmission network. From the previous analysis, an evolutionary particle swarm optimization algorithm is applied to find the preferred solution, in terms of DES site and size. The optimization model includes a multi-interval simulation of the network, for a given period of time, in order to include both power and energy requirements. IEEE 14-bus Reliability Test System, with energy storage, is simulated as case study.

A multiyear dynamic transmission expansion planning model using a discrete based EPSO approach

Today, with the increase of distributed generation sources in power systems, it’s important to optimal location of these sources. Determine the number, location, size and type of distributed generation (DG) on Power Systems, causes the reducing losses and improving reliability of the system. In this paper is used Co-evolutionary particle swarm optimization algorithm (CPSO) to determine the optimal values of the listed parameters. Obtained results through simulations are done in MATLAB software is presented in the form of figure and table in this paper. These tables and figures, show how to changes the system losses and improving reliability by changing parameters such as location, size, number and type of DG. Finally, the results of this method are compared with the results of the Genetic algorithm (GA) method, to determine the performance of each of these methods. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7026 Full Text: PDF

Increasingly large-scale clusters of PC/WS continue to become majority platform in HPC field. Such a commodity cluster environment, there may be incremental upgrade due to several reasons, such as rapid progress in processor technologies, or user needs and it may cause the performance heterogeneity between nodes from which the application programmer will suffer as load imbalances. To overcome these problems, some dynamic load balancing mechanisms are needed. In this paper, we report our ongoing work on dynamic load balancing extension to Omni/SCASH which is an implementation of OpenMP on Software Distributed Shared Memory, SLASH. Using our dynamic load balancing mechanisms, we expect that programmers can have load imbalances adjusted automatically by the runtime system without explicit definition of data and task placements in a commodity cluster environment with possibly heterogeneous performance nodes.

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.