Optimization of distribution network configuration with multi objective function based on improved cuckoo search algorithm

This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective.

Security of the media information such as image and video is one of the fundamental prerequisites for broadcasting communications and computer systems. Most of image security system, for famous systems, similar to the Internet, is not reasonable for wireless sensor systems, requesting legitimate examination around there. In the proposed investigation, the security of Digital Images (DI) is upgraded by utilizing a Lightweight encryption algorithm. To ensure security, the images selected for security investigation was shared by a number of copies. With the help of the presented share creation model, for example, Chinese Remainder Theorem (CRT), the image was encoded into a number of shares and the created shares were highly secured by the proposed Stream Encryption (SE) algorithm. The metaheuristic algorithm was introduced by passing the selection of optimal public and private keys to encrypt as well as decrypt the image in secure transmission. With the help of Improved Cuckoo Search Algorithm (ICSA), the optimal keys were selected with fitness function as maximum throughput. The proposed SE-based ICSA algorithm consumed the least time in creating key value to decrypt the image in WSN security model. The simulation result exhibited that the SE-based ICSA algorithm enhanced the exactness of DI security for all input images (Lena, Barbara, baboon, house, and airplane) when compared with existing algorithms.

The research aims to improve prediction accuracy for heights of fractured water-conducting zones (FWCZs) and effectively prevent and control roof water disasters, to ensure safe coal mining. For this purpose, the method that integrates the improved cuckoo search (ICS) algorithm and extreme learning machine (ELM) is used to predict heights of FWCZs. Based on an analysis of factors influencing FWCZs, the ICS algorithm is employed to optimize two key parameters of the ELM model, the input weight ѡ and the bias b of hidden elements, thus establishing the ICS–ELM model for predicting the height of the FWCZ. The ICS–ELM model is trained using 42 measured samples, and the trained model is employed to predict the remaining six sample data points. The obtained prediction results show a relative error of only 3.97% and are more consistent with the actual situation. To verify the effectiveness of the model, the prediction results are compared with those of the adaptive particle swarm optimization based least squares support vector machine (APSO–LSSVM) and particle swarm optimization (PSO) based backpropagation (PSO–BP) models. The average relative errors of the two models are 8.21 and 9.75%, respectively, which further proves that the ICS–ELM model improves the accuracy of prediction results for heights of FWCZs. The heights of FWCZs predicted using the model are accurate and reliable, and the accuracy meets the requirements of engineering practice.

This paper proposes a novel fault location (FL) technique on a double-circuit transmission line (DCTL) based on an optimization problem. This proposal is particularly prominent when only measurements of voltages and currents are required at the two terminals of the DCTL of during- and pre-fault periods. Then an improved cuckoo search (ICS) is proposed to find out the optimization solution for locating faults on the DCTL. The ICS algorithm takes the advantage of the chaos theory for improving the algorithm performance in the exploration and exploitation procedure. A comparison between the FL results of applying the ICS, CS, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) is implemented to confirm the effectiveness of the proposal.

Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems. However, the deep learning algorithms often contain some hyper-parameters which may be continuous, integer, or mixed, and are often given based on experience but largely affect the effectiveness of activity recognition. In order to adapt to different hyper-parameter optimization problems, our improved Cuckoo Search(CS) algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm. The algorithm optimizes the hyper-parameters in the deep learning model robustly, and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal. Then, the mixed hyper-parameter in Convolutional Neural Network (CNN), Long-Short-Term Memory(LSTM) and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets. Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not, we can get a better deep learning model using our method.

Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter sensitivity, this study proposes an SNN model optimized by an Improved Cuckoo Search (ICS) algorithm (termed ICS-SNN). The ICS algorithm enhances global search capability through piecewise-mapping-based population initialization and introduces a dynamic discovery probability mechanism that adaptively increases with iteration rounds, thereby balancing exploration and exploitation. Applied to futures market price difference prediction, experimental results demonstrate that ICS-SNN achieves reductions of 13.82% in MAE, 21.27% in MSE, and 15.21% in MAPE, while improving the coefficient of determination (R2) from 0.9790 to 0.9822, compared to the baseline SNN. Furthermore, ICS-SNN significantly outperforms mainstream models such as Long Short-Term Memory (LSTM) and Backpropagation (BP) networks, reducing prediction errors by 10.8% (MAE) and 34.9% (MSE), respectively, without compromising computational efficiency. This work highlights that ICS-SNN provides a biologically plausible and computationally efficient framework for complex financial TSF, bridging the gap between neuromorphic principles and real-world financial analytics. The proposed method not only reduces manual intervention in hyperparameter tuning but also offers a scalable solution for high-frequency trading and multi-modal data fusion in future research.

A rate-dependent asymmetric Prandtl–Ishilinskii (RAPI) model was proposed to tackle the serious rate-dependent hysteresis nonlinearity of the giant magnetostrictive actuator(GMA) output. First, a polynomial function was introduced based on the PI model, and hysteresis factors were introduced to the Play operator, which accurately described the asymmetrical characteristic of the actuator output. On this basis, rate-dependent parameters were added to establish a rate-dependent RAPI model. Second, an improved cuckoo search (ICS) algorithm was proposed to solve the difficulty in the parameter identification of the RAPI model. For the ICS algorithm, the algorithm stability and optimization accuracy were improved using the adaptive step (AS) strategy and bird’s nest disturbance strategy. Then, the effectiveness of the ICS algorithm was tested by comparing it with other parameter identification algorithms. Finally, the rate-dependent RAPI model was verified by combining the output data of the giant magnetostrictive actuator under different frequencies. The results show that the rate-dependent RAPI model exhibits a higher accuracy than the PI model, thus verifying the effectiveness of the rate-dependent RAPI model.

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.

The proposed approach presents a hybrid evolutionary algorithm to overcome the Distribution Network Reconfiguration (DNR) problem. This approach combines the Chaotic Particle Swarm Optimization (CPSO) and Teaching-Learning-Based Optimization (TLBO) to find the global optima in more efficient way. Similar to the other evolutionary algorithms, in order to achieve a proper performance, PSO has some parameters i.e. inertia weight factor, which should be adjusted. But the TLBO is free from adjusting parameters and find the optimum solution without tuning any parameters. In order to overcome the problem of the tuning of the PSO algorithms a chaotic framework is implemented to tune the inertia weight factor dynamically. The learning factors of PSO algorithm are considered as a fix number. The CPSO is mixed to TLBO in order to improve the quality of solutions. The obtained hybrid algorithm is applied to minimize the electrical power loss of distribution network by usage of the network reconfiguration. To validate the effectiveness of the proposed algorithm it is applied to two test systems.

To cope with increasingly serious three-phase disequilibrium in distribution network, a reconfiguration model is proposed for distribution network with the optimization objective of reducing three-phase disequilibrium. First by analyzing the distribution network in topology, the distribution network reconfiguration problem is converted into a problem of constructing spanning tree of the graph, which is solved by breaking-cycle-basis method. Then by selecting the first-branch randomly and canceling the heuristic value of network, an improved ant colony optimization algorithm is proposed to solve the reconfiguration problem, which can expand the search extent and avoid search stagnation. Specific examples of verification prove that the proposed algorithm can obtain the global best solution with less computation time and higher probability compared to the conventional methods.

Dynamic reconfiguration of distribution network based on dynamic optimal period division and multi-group flight slime mould algorithm

With the continuous development of society, the demand for electricity is gradually increasing, and the power industry has developed rapidly. However, the efficiency of power grid monitoring often decreases due to long working hours, which can not guarantee the security and stability of the entire power grid system. It is of great practical significance to improve the efficiency of power grid monitoring, ensure the normal operation of power equipment and ensure the safety of power use while satisfying the users’ needs. This paper will analyze the current situation of genetic algorithm and solve the problem of distribution network reconfiguration, and put forward effective measures to improve smart grid monitoring. In order to ensure the safe and stable operation of the power grid, optimize the monitoring business and improve the monitoring efficiency, it is of great significance.

Network reconfiguration for load balancing is the determination of switching-options for a particular set of loads on a distribution system that improves load balancing the most and is performed altering the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie-switches. By transferring loads from the heavily loaded feeders to the lightly loaded ones, network reconfiguration can balance feeder loads and alleviate overload conditions of a network. In this paper a novel method is proposed by formulating an algorithm to reconfigure distribution networks for load balancing. Systematic logical approach is used to determine the feasible switching combinations, select the status of the switches, and find the best combination of switches for maximum improvement in load balancing. The algorithm has been developed in two stages. In the first stage, a limited number of switching combinations is formed by trinary logic principle. A simultaneous switching approach is applied logically to find the best combination by selecting the appropriate switching options, connecting the switches accordingly, and assigning the loads for the newly formed combinations. In the next stage, the search is extended to investigate whether any other switching combination gives rise to maximum improvement in load balancing, compared to the improvement obtained in the first stage. This is carried out by considering one after another, the branches next to the open-branches of the best configuration, and the switching configuration that gives the maximum improvement in load balancing is identified. The proposed method has been tested on a 69-bus test system, and the test results indicate that the algorithm proposed is able to determine the appropriate switching-options of the optimal (or near optimal) configuration with less computation. This paper proposes a scheme to implement the simultaneous switching algorithm for real-time on-line control of three-phase distribution networks, with symmetrical loading per phase.