Search Results (8)

A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (ESC). Firstly, an adaptive perturbation factor strategy was employed to maintain population diversity. Secondly, introducing a restart mechanism to enhance the exploration capability of mESC. Finally, a dynamic centroid reverse learning strategy was designed to balance local development. In addition, in order to accelerate the global convergence speed, a boundary adjustment strategy based on the elite pool is proposed, which selects elite individuals to replace bad individuals. Comparing mESC with the latest metaheuristic algorithm and high-performance winner algorithm in the CEC2022 testing suite, numerical results confirmed that mESC outperforms other competitors. Finally, the superiority of mESC in handling problems was verified through several classic real-world optimization problems. Full article

To cope with common local optimum traps and balance exploration and development in complex multi-peak optimisation problems, this paper puts forth a Dual-Performance Multi-subpopulation Adaptive Restart Differential Evolutionary Algorithm (DPR-MGDE) as a potential solution. The algorithm employs a novel approach by utilising the fitness and historical update frequency as dual-performance metrics to categorise the population into three distinct sub-populations: PM (the promising individual set), MM (the medium individual set) and UM (the un-promising individual set). The multi-subpopulation division mechanism enables the algorithm to achieve a balance between global exploration, local exploitation and diversity maintenance, thereby enhancing its overall optimisation capability. Furthermore, the DPR-MGDE incorporates an adaptive cross-variation strategy, which enables the dynamic adjustment of the variation factor and crossover probability in accordance with the performance of the individuals. This enhances the flexibility of the algorithm, allowing for the prioritisation of local exploitation among the more excellent individuals and the exploration of new search space among the less excellent individuals. Furthermore, the algorithm employs a collision-based Gaussian wandering restart strategy, wherein the collision frequency serves as the criterion for triggering a restart. Upon detecting population stagnation, the updated population is subjected to optimal solution-guided Gaussian wandering, effectively preventing the descent into local optima. Through experiments on the CEC2017 benchmark functions, we verified that DPR-MGDE has higher solution accuracy compared to newer differential evolution algorithms, and proved its significant advantages in complex optimisation tasks with the Wilcoxon test. In addition to this, we also conducted experiments on real engineering problems to demonstrate the effectiveness and superiority of DPR-MGDE in dealing with real engineering problems. Full article

In order to solve the problems of traditional high-voltage power supply ignition protection circuits, such as non-essential start–stop power supply, a slow response speed, the system needing to be restarted manually, and so on, a high-voltage power supply system self-recovery ignition protection circuit was designed using an IGWO (improved grey wolf optimization) and PID control strategy designed to speed up the response speed, and improve the reliability and stability of the system. In high-voltage power supply operation, the firing discharge phenomenon occurs. Current transformers fire signal into a current signal through the firing voltage value and Zener diode voltage comparison to set the safety threshold; when the threshold is exceeded, the fire protection mechanism is activated, reducing the power supply voltage output to protect the high-voltage power supply system. When the ignition signal disappears, based on the IGWO-PID control of the ignition self-recovery circuit according to the feedback voltage, the DC supply voltage of the high-voltage power supply is adjusted, inhibiting the ignition discharge and, according to the ignition signal, “segmented” to restore the output of the initial voltage. MATLAB/Simulink was used to establish a system simulation model and physical platform test. The results show that the protection effect of the designed scheme is an improvement, in line with the needs of practical work. Full article

Shutdowns of photovoltaic installations are a problem that has been increasingly affecting private investors who have built home installations of several to a dozen kWp over the last few years. This problem, most often caused by outdated infrastructure, appears in many countries and impacts energy production. This work focuses on three aspects of the problem. The first one answers the question of how shutdowns of the photovoltaic installation affect production, and how significant the energy loss happens when the PV inverter is not working or is in the restart phase. The second aspect proposes an original, low-cost method that reduces the number of shutdowns. This method relates to the auto-consumption mechanism associated with domestic water heaters and the system for measuring voltage and energy consumption from the electrical network. The solution is based on constant monitoring of the network voltage and the switching of heaters based on a dedicated algorithm. Additionally, continuous analysis also allows for reporting observed irregularities to the electricity supplier. The third and final factor corresponds to the real impact of shutdowns on the long-term aspect of the investment and the extension of its payback period, and to what extent the proposed solution shortens this period. Through a detailed analysis on the issue of shutdowns, the proposed solution allows for a reduction in the number of shutdowns by over 40%. However, due to the fact that it discusses a specific case, this solution requires a calibration and adjustment process, which is discussed in the article. Full article

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly. Full article

Aiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mechanism. When the population falls into local optimal or premature convergence, the restart strategy is activated to expand the search range by re-randomly initializing the group particles. An inverted S-type decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the ability of local search and global search. Finally, the new RDS-PSO algorithm is combined with cubic spline interpolation to apply to the path planning and smoothing processing of mobile robots, and the coding mode based on the path node as a particle individual is constructed, and the penalty function is selected as the fitness function to solve the shortest collision-free path. The comparative results of simulation experiments show that the RDS-PSO algorithm proposed in this paper solves the problem of falling into local extremums and precocious puberty, significantly improves the optimization, speed, and effectiveness of the path, and the simulation experiments in different environments also show that the algorithm has good robustness and generalization. Full article

Fast iterative soft threshold algorithm (FISTA) is one of the algorithms for the reconstruction part of compressed sensing (CS). However, FISTA cannot meet the increasing demands for accuracy and efficiency in the signal reconstruction. Thus, an improved algorithm (FIPITA, fast iterative parametric improved threshold algorithm) based on mended threshold function, restart adjustment mechanism and parameter adjustment is proposed. The three parameters used to generate the gradient in the FISTA are carefully selected by assessing the impact of them on the performance of the algorithm. The developed threshold function is used to replace the soft threshold function to reduce the reconstruction error and a restart mechanism is added at the end of each iteration to speed up the algorithm. The simulation experiment is carried out on one-dimensional signal and the FISTA, RadaFISTA and RestartFISTA are used as the comparison objects, with the result that in one case, for example, the residual rate of FIPITA is about 6.35% lower than those three and the number of iterations required to achieve the minimum error is also about 102 less than that of FISTA. Full article

Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants. Full article