Local Optimal Solution Research Articles

Fireworks algorithm with elitism-based selection strategy and optimal particle guidance mechanism

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved Fireworks Algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions.

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

Vehicle routing problem for fresh products distribution considering customer satisfaction through adaptive large neighborhood search

To deal with the fresh products distribution problem in city logistics, this paper focuses on the customer psychological behavior considering both delivery time and the fresh products quality from the perspective of customer satisfaction. Thereby, we propose a mixed-integer nonlinear programming model, among which a time satisfaction function depending on customers’ time windows and a quality satisfaction function considering the freshness of the products are introduced as constraints. Then, an improved Adaptive Large Neighborhood Search algorithm is designed with a new strategy to jump out of the local optimal solutions and with new operators considering satisfaction functions. Numerical experiments on benchmark instances of standard Solomon instances demonstrate the effectiveness and the efficiency of the proposed method. The calculation results showed that the proposed algorithm is superior to previous approaches by obtaining better feasible solutions with less calculation time. Furthermore, managemental insights are provided for enterprises on making wise investments while ensuring customer experience through optimizing distribution strategies.

Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization

The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it’s called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.

Improved electrical coupling integrated energy system based on particle swarm optimization

The rational utilization of energy is an important issue for sustainable development. Electrically coupled integrated energy systems can enhance energy utilization efficiency and reduce energy costs. However, traditional integrated energy system optimization has problems with local optima and slow convergence speed, which cannot fully utilize energy resources. Therefore, this study proposes an improved electrical coupling integrated energy system on the ground of particle swarm optimization algorithm. In response to the problems of local optima and slow convergence speed in traditional optimization algorithms, particle swarm optimization algorithm is introduced for system optimization. By combining PSO with simulated annealing algorithm, the possibility of PSO in global optimization is reduced. The local search ability of PSO and the global search ability of simulated annealing algorithm are used to find the optimal solution. The particle swarm optimization algorithm is used for preliminary search. When the particle falls into the local optimal, the simulated annealing algorithm is introduced for global search, and the particle is guided to jump out of the local optimal and continue searching. The experiment demonstrates that the improved algorithm has certain advantages in solving test functions. The variance, mean, and optimal values are 0.00125, 0.13874, and 0.105531, respectively, which are all better than the particle swarm optimization algorithm. The simulated annealing algorithm improved the particle swarm optimization algorithm with a high accuracy index, which eventually stabilized above 0.9, and the recall index also remained above 0.8. After 100 iterations, it had already fallen into a local optimal solution. By applying the improved hybrid optimization algorithm to the electrically-coupled integrated energy system, the distribution of various energy sources can be managed and optimized more effectively, and the overall performance of the system can be improved. Especially when dealing with complex energy scheduling and distribution problems, the algorithm can provide more stable, efficient and reliable solutions. This study can achieve efficient operation and optimized scheduling of integrated energy systems, reduce energy consumption and environmental pollution, and reduce energy costs. And it can improve the reliability and stability of energy supply, which has important application value and research significance.

A food quality detection method based on electronic nose technology

Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of convolutional neural network combined with wavelet scattering network (CNN-WSN) and improved seahorse optimizes kernel extreme learning machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of CNN are fused with the scattering features of WSN, and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the seahorse optimisation algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the KELM model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.

Research on path planning of tea picking robot based on ant colony algorithm

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.

Mining the colossal patterns using ISSA based KMC with VGHHO clustering model for high dimensional data

High-dimensional datasets comprise a few rows but a huge total of features. The increased difficulty data scientists have in efficiently gleaning insights from high-dimensional large data is a direct outcome of this trend. Therefore, a K-Means clustering method (KMC) must be implemented to cleanse this data, with the centroid of KMC being ideally chosen by improved squirrel search algorithm (ISSA). The suggested algorithm features not one but two different types of searches: the leaping search and the progressive search. The linear regression selection strategy is utilised to automatically choose the applicable approach during the evolutionary phase; this increases SSA's stability. Harris Hawks Optimizer (HHO), which replicates the behaviour of a Harris hawk during rabbit predation, is applied to the cleaned data to cluster the massive pattern. However, HHO has problems with low accuracy and early convergence because of its inability to strike a good balance among exploitation. A new variant of HHO, dubbed velocity-guided HHO (VGHHO), including three improvements is proposed to address these drawbacks. By including a velocity operator and an inertia weight into the search equation, we are able to create a unique modified position search equation for use during the exploitation phase. Then, we incorporate a learning mechanism based on refraction and opposition to provide the promising resolutions and aid the swarm in escaping the local optimal solution. After the massive cluster has been built with VGHHO, uproot technology is used to uncover massive patterns. We run the tests on a wide variety of high-dimensional datasets and employ a number of different efficiency metrics. Evidence from these investigations shows that the proposed method produces high-quality mining results.

A State-of-Health Estimation Method for Lithium Batteries under Multi-Dimensional Features

In recent years, the number of new energy vehicles has increased rapidly. The online state-of-health (SOH) prediction of lithium-ion batteries, which are core components of new energy vehicles, is crucial for maintaining vehicle safety. However, existing data-driven methods encounter challenges such as the difficult application of health feature extraction methods in practice, single feature dimensions, and complex algorithm models. This study extracted the peak height of the incremental capacity (IC) curve, constant-current charging time, and time when the battery surface temperature reaches its maximum value as health features from multiple dimensions. Furthermore, by randomly generating prey, the Pelican Optimization Algorithm (POA) fundamentally overcomes the shortcomings of traditional swarm intelligence optimization algorithms which are prone to falling into local optimal solutions. POA was introduced to optimize the Deep Extreme Learning Machine (DELM), which maximally simplified the algorithm model while ensuring accuracy. The experimental results demonstrate that this method does not require extensive historical data support. Whether applied to batteries under the same or different working conditions, all four battery groups exhibit excellent prediction results, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values below 1.2%.

Quantum‐Behaved Particle Swarm Optimization Based on Concentration Selection Probability Assignment Weights for Power System Economic Dispatch

The economic dispatch (ED) problem is very important in the economics of power systems, and a suitable dispatch algorithm can help power plants save huge amounts of money and use energy efficiently. To ensure the economics of the power system dispatch strategy, this paper proposes a quantum‐behaved particle swarm improvement algorithm for solving the ED problem. First, the algorithm addresses the problem that the classical quantum‐behaved particle swarm optimization (QPSO) tends to be premature and falls into local optimality. We propose a method to calculate the mean best position by assigning weights to different particles, by adopting the idea of concentration selection in the immune concentration regulation mechanism, and designing a Weight‐Concentration selection probability function, so that those particles located outside the local optimal solution region but with smaller fitness value and better evolutionary trend can be retained. Second, to make the algorithm focus on global search at the beginning of the iteration and more focused search at the end to enhance the neighborhood search capability at the later stage, a decreasing function of the slope K in the Weight‐Concentration selection probability function is designed so that the influence of the weighting term on the calculation of the mean best position decreases gradually. The algorithm is evaluated with four power systems, and the results show that the overall performance of the improved algorithm is better than other algorithms in solving the ED problem in terms of minimum solution, convergence, and stability. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting.

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU's sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.

Enhancing diabetic retinopathy detection through preprocessing and feature extraction with MGA-CSG algorithm

Anticipatory monitoring of diabetic retinal (DR) disease is crucial in preventing vision loss and blindness, making it a leading cause of worldwide vision impairment. In this study, we propose a novel technique, the Modified Generative Adversarial-based Crossover Salp Grasshopper (MGA-CSG) approach, for early prediction and accurate classification of diabetic retinal diseases using fundus image datasets. The proposed MGA-CSG approach combines deep learning with an optimization algorithm to achieve superior classification accuracy. We start with pre-processing of data to attenuate image variation, convert intensities, denoise, and enhance contrast in the fundus images. Next, a Convolutional Neural Network (CNN) is used for feature extraction, capturing essential information from the images. To address the limited amount of feature vectors generated by the CNN and improve learning uncertainty, we employ a Generative Adversarial Network (GAN) model to augment the feature vectors. It generates additional images, allowing the classifier to train with fewer input samples, leading to improved classification performance. One of the key novelties of our approach lies in the utilization of both labeled and unlabeled data samples, enhancing the GAN training performance. We replace the binary classifier in the GAN with a multiclass classifier, enabling separate discrimination of retinal disease classes. The MGA-CSG approach leverages the crossover Grasshopper Optimizer Algorithm (GOA) and Salp Swarm Algorithm (SSA) to optimize the model and avoid falling into local optimal solutions. This optimization process further enhances the accuracy of diabetic retinal disease classification. Experimental results demonstrate the superiority of the proposed MGA-CSG approach, achieving high accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient in predicting glaucoma, diabetic retinopathy, cataract, macular edema, and myopia. The classification achieved impressive accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient of 98.8%, 98.7%, 96.5%, 97.1%, 98.2%, and 95%, respectively. Overall, our novel MGA-CSG approach offers an efficient and accurate method for early screening and classification of diabetic retinal diseases. The combination of CNN-based feature extraction, GAN-based augmentation, and MGA-CSG optimization contributes to the model's high performance. This approach holds significant potential in improving healthcare management and providing timely interventions for patients with retinal diseases.

A Particle-Swarm-Optimization-Algorithm-Improved Jiles–Atherton Model for Magnetorheological Dampers Considering Magnetic Hysteresis Characteristics

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles–Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.

Segmental Regularized Constrained Inversion of Transient Electromagnetism Based on the Improved Sparrow Search Algorithm

The initial inversion model is typically established in a transient electromagnetic nonlinear inversion, assuming the accurate capture of the number of layers in the geoelectric model; however, this assumption leads to significantly poorer inversion results for complex models when obtaining the exact number of layers from available a priori information, which is challenging. This study proposes a segmented regularized inversion method to enhance inversion accuracy and stability under varying conditions. The process involves two key steps: Firstly, a segmented initial model is established based on preliminary information. The layering criteria and layer thickness threshold for each segment are set during inversion to reduce dependence on the accuracy of the preliminary information. Secondly, a segmented regularization constraint is added to the objective function to improve the efficiency and stability of the inversion, as numerous parameters can exacerbate the problem of inversion ambiguity. Subsequently, an improved sparrow search algorithm (ISSA) is utilized to optimize the inversion objective function. This enhances the efficiency of searching for the objective function and the algorithm’s ability to escape local optimal solutions. The proposed method is evaluated using one-dimensional and two-dimensional models with different initial models and inversion algorithms and applied to the inversion of on-site exploration data in a coal mining area in Chongqing. Comparative results demonstrate that the proposed segmented regularization method, based on the improved sparrow search algorithm, exhibits superior practicality and a higher fitting accuracy.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

Joint Precoding Design for Sub-Connected Hybrid Beamforming System

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

Energy flow optimization of an electric-gas-heat integrated energy system based on the energy circuit theory

The energy flow optimization of the integrated energy system (IES) can not only decrease the operating cost of the IES but also maximize the utilization rate of renewable energy. To deal with the problem that the optimization results fall into the local optimal solution due to the over-limit of IES state variables, based on the IES energy circuit theory, this paper established an energy flow optimization model of the electric-gas-heat integrated energy system by taking the weighted sum of the operating cost, state variables over-limit penalty cost, and wind and light abandonment penalty cost as the optimization objective. The growth optimizer (GO) algorithm can avoid the local convergence of the optimization results and accelerate the solving speed of the model. Therefore, the effectiveness and feasibility of the model and the optimization method are confirmed by the simulation example.

Electric vehicle charging optimization strategy based on the mopso algorithm

Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.

Two-stage evolutionary algorithm with fuzzy preference indicator for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) are multi-objective optimization problems (MOPs) involving multiple equivalent global or local Pareto optimal solution sets (PSs). For decision-makers, not only the global optimal solution sets need to be found, but also the value of local optimal solution sets cannot be ignored. However, most multimodal multi-objective evolutionary algorithms (MMOEAs) tend to select solutions with better convergence, and it is difficult to obtain the global PSs and local PSs at the same time. Therefore, we propose a fuzzy preference indicator-based two-stage evolutionary algorithm (FPITSEA) in this paper. To evaluate more comprehensively the potential of each solution in the population for locating the global and local PS during the evolutionary process, a fuzzy preference indicator is designed in FPITSEA. The fuzzy preference indicator is used to guide the evolution of the population in the first stage to find the global and local Pareto optimal regions. Subsequently, an independent evolution strategy is implemented in the second stage to distinguish different PSs as accurately as possible while also ensuring the convergence quality of the solution set. In addition, an improved distance-based subset selection method is proposed, aiming to simultaneously improve the distribution of the solution set in the decision space and objective space. Experimental results on several test sets of MMOPs show the advantages of FPITSEA over several state-of-the-art MMOEAs.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.