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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. The work was based on multi-regional peak and valley data, and we selected Lanzhou, Guiyang, Beijing, Guangzhou, Shanghai, and Nanjing as typical representatives for systematic validation and analysis. Our findings were as follows: (1) The model demonstrated excellent convergence and stability during the electricity price optimization process, particularly under flat-rate price conditions. This model effectively avoided local optima traps and enhanced global search capability, achieving the dual goals of rapid convergence and high stability, and showed exceptional optimization efficiency and adaptability; (2) building upon its optimization performance, the model further improved the uniformity and diversity of the solution distribution, ensuring robustness and flexibility in global search ability. Moreover, by dynamically adjusting the price function and multi-level evaluation system, the model significantly optimized price elasticity, time-of-use pricing regulation efficiency, energy consumption paths, and the operational stability of the distribution network. The model exhibited high resilience and fine-grained control capabilities in the complex electricity market; (3) finally, based on the optimized electricity price strategy derived from training, the model reduced electricity costs and price volatility. Moreover, its superior performance in economic benefits and market adaptability was comprehensively validated through high-precision power consumption forecasting. We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.

Due to rapid socioeconomic growth and current environmental concerns, reducing global greenhouse gas emissions is a key step toward sustainable development. In recent years, some researchers have begun to adopt new grid management technologies such as virtual power plants (VPP) that allow them to improve energy management and cost and reduce gas emissions. The integration of distributed energy resources (DER), such as solar photovoltaic and wind power, combined with micro‐turbines and energy storage systems and with the support of VPP intelligence, will contribute immensely to improving micro‐grids (MGs) energy performance and reducing gas emissions. In this paper, an expert multi‐objective feasibility enhanced particle swarm optimization algorithm is adopted for the optimal scheduling of energy management system to reduce the total operating cost and net emission simultaneously. The proposed algorithm is tested on an Algerian reel building‐MG integrated VPP with DERs and combined cooling, heat, and power generation. Finally, different cases are simulated for a 24‐h time and discussed to demonstrate the validity of the proposed VPP energy system and the effectiveness of the adopted algorithm to reduce gas emissions and the total operating cost. Micro‐grid energy management system virtual power plant.

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality.

In order to obtain geotechnical engineering material mechanical parameters correctly by using back analysis and overcome shortcoming of ordinary Particle Swarm Optimization, Improved Particle Swarm Optimization (IPSO) algorithm is developed on the aspects such as Stretching Particle, Metropolis Algorithm and adaptive weight updating .at the same time, the algorithm is compared with Catastrophe Particle Swarm Optimization Algorithm (CPSO) and Quantum Particle Swarm Optimization Algorithm(QPSO). Also result of back analysis was compared with that of Ultrasonic Testing and that of mixed-model of dam monitoring. The analysis shows that IPSO has better performance than that of PSO and CPSO, and considerable performance with QPSO.

Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.

This study presents a repetitive control method based on a multi-stage particle swarm optimization (PSO) algorithm with variable intervals to enhance the tracking performance of Takagi–Sugeno (T–S) fuzzy systems. First, a T–S fuzzy model is used to describe a nonlinear system. A modified repetitive control structure with two repetitive loops guarantees the tracking accuracy of periodic signals. Taking advantage of the two-dimensional (2D) property with continuous control and discrete learning, a continuous-discrete 2D model is presented to describe the nonlinear repetitive control system. Next, a multi-stage PSO algorithm with variable intervals searches for the best parameter combination in the linear matrix inequality-based stability condition to regulate the control and learning actions, which avoids a suboptimal solution and guarantees high control accuracy. Finally, an application to control the speed of synchronous motor with a permanent magnet demonstrates the validity of the method, and comparisons with related methods show its superiority.

Considering that the excitation method of an electric excitation synchronous motor has the disadvantages of the brush and slip ring, this article proposes a new brushless excitation system, which includes two parts: a wireless charging system and a motor. To meet the requirements of maximum transmission efficiency and constant voltage output of the system, a bilateral cooperation control strategy is proposed. For the strategy, the buck converter in the receiving side of the system can maintain maximum transmission efficiency through impedance matching, while the inverter in the transmitting side can keep the output voltage constant through phase shift modulation. In the control process, considering that the offset of coupling coils will affect the control results, a grey wolf optimization–particle swarm optimization algorithm is proposed to identify mutual inductance. Simulation and experimental results show that this identification algorithm can improve the identification accuracy and maximize the avoidance of falling into local optima. The final experimental result shows that the bilateral cooperation control strategy can maintain the output voltage around 48 V and the transmission efficiency around 84.5%, which meets the expected requirements.