Strong Global Search Capability Research Articles

Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy

The Electric Fish Optimization (EFO) algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters, high robustness, and strong global search capabilities. Nevertheless, when operating in complex environments, the EFO algorithm encounters several challenges including premature convergence, susceptibility to local optima, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO). This hybrid approach incorporates the Golden Sine Algorithm and good point set theory to augment the EFO algorithm’s capabilities, employs a variable-step-size Levy flight strategy to efficiently address passive electric field localization stagnation problems, and utilizes a standstill label strategy to mitigate the algorithm’s tendency to fall into local optima during the iterative process. By leveraging multiple solutions to optimize the EFO algorithm, this framework enhances its adaptability in complex environments. Experimental results from benchmark functions reveal that the proposed SLLF-EFO algorithm exhibits improved performance in complex settings, demonstrating enhanced search speed and optimization accuracy. This comprehensive optimization not only enhances the robustness and reliability of the EFO algorithm but also provides valuable insights for its future applications.

Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection

When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted.Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability.

A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems.

Research on Wind Resistance Optimization Method for Cable-Stiffened, Single-Layer Spherical Reticulated Shell Based on QPSO Algorithm

This study proposes an improved mixed-variable quantum particle swarm optimization (QPSO) algorithm capable of optimizing both continuous and discrete variables. The algorithm is applied to the wind resistance optimization of a cable-stiffened, single-layer spherical reticulated shell (SLSRS), optimizing discrete variables like member dimensions and cable dimensions alongside continuous variables such as cable prestress. Through a computational case study on an SLSRS, the optimization results of the proposed QPSO method are compared with other optimization techniques, validating its accuracy and reliability. Furthermore, this study establishes a mathematical model for the wind resistance optimization of cable-stiffened SLSRSs and outlines the wind resistance optimization process based on the mixed-variable QPSO algorithm. The optimization of these structures reveals the strong stability and global search capabilities of the proposed algorithm. Additionally, the comparison of section optimization and shape optimization highlights the significant impact of the shell shape on steel usage and costs, underscoring the importance of shape optimization in the design process.

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm’s exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed multi-objective evolutionary NAS framework and the practicality and transferability of the introduced bi-population MOEA/D-based NAS method compared to existing state-of-the-art NAS methods.

Bat algorithm based on kinetic adaptation and elite communication for engineering problems

AbstractThe Bat algorithm, a metaheuristic optimization technique inspired by the foraging behaviour of bats, has been employed to tackle optimization problems. Known for its ease of implementation, parameter tunability, and strong global search capabilities, this algorithm finds application across diverse optimization problem domains. However, in the face of increasingly complex optimization challenges, the Bat algorithm encounters certain limitations, such as slow convergence and sensitivity to initial solutions. In order to tackle these challenges, the present study incorporates a range of optimization components into the Bat algorithm, thereby proposing a variant called PKEBA. A projection screening strategy is implemented to mitigate its sensitivity to initial solutions, thereby enhancing the quality of the initial solution set. A kinetic adaptation strategy reforms exploration patterns, while an elite communication strategy enhances group interaction, to avoid algorithm from local optima. Subsequently, the effectiveness of the proposed PKEBA is rigorously evaluated. Testing encompasses 30 benchmark functions from IEEE CEC2014, featuring ablation experiments and comparative assessments against classical algorithms and their variants. Moreover, real‐world engineering problems are employed as further validation. The results conclusively demonstrate that PKEBA exhibits superior convergence and precision compared to existing algorithms.

UAV Complex-Scene Single-Target Tracking Based on Improved Re-Detection Staple Algorithm

With the advancement of remote sensing technology, the demand for the accurate monitoring and tracking of various targets utilizing unmanned aerial vehicles (UAVs) is increasing. However, challenges such as object deformation, motion blur, and object occlusion during the tracking process could significantly affect tracking performance and ultimately lead to tracking drift. To address this issue, this paper introduces a high-precision target-tracking method with anomaly tracking status detection and recovery. An adaptive feature fusion strategy is proposed to improve the adaptability of the traditional sum of template and pixel-wise learners (Staple) algorithm to changes in target appearance and environmental conditions. Additionally, the Moth Flame Optimization (MFO) algorithm, known for its strong global search capability, is introduced as a re-detection algorithm in case of tracking failure. Furthermore, a trajectory-guided Gaussian initialization technique and an iteration speed update strategy are proposed based on sexual pheromone density to enhance the tracking performance of the introduced re-detection algorithm. Comparative experiments conducted on UAV123 and UAVDT datasets demonstrate the excellent stability and robustness of the proposed algorithm.

Competitive Knowledge Transfer–Enhanced Surrogate-Assisted Search for Production Optimization

Summary Production optimization is a crucial component of closed-loop reservoir management, which typically aims to search for the best development scheme for maximum economic benefit. Over the decades, a large body of algorithms have been proposed to address production optimization problems, among which the surrogate-assisted evolutionary algorithm (SAEA) gained much research popularity due to its problem information-agnostic implementation and strong global search capability. However, existing production optimization methods often optimize individual tasks from scratch in an isolated manner, ignoring the available optimization experience hidden in previously optimized tasks. The incapability of transferring knowledge from possibly related tasks makes these algorithms always require a considerable number of simulation runs to obtain high-quality development schemes, which could be computationally prohibitive. To address this issue, this paper proposes a novel competitive knowledge transfer (CKT) method to leverage the knowledge from previously solved tasks toward enhanced production optimization performance. The proposed method consists of two parts: (1) similarity measurement that uses both reservoir features and optimization data for identifying the most promising previously solved task and (2) CKT that launches a competition between the development schemes of different tasks to decide whether to trigger the knowledge transfer. The efficacy of the proposed method is validated on a number of synthetic benchmark functions as well as two production optimization tasks. The experimental results demonstrate that the proposed method can significantly improve production optimization performance and achieve better optimization results when certain helpful previously optimized tasks are available.

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.

Fault Recovery of Distribution Network with Distributed Generation Based on Pigeon-Inspired Optimization Algorithm

In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss.

Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm

Aiming at the problem of high energy consumption caused by the nonlinear, strong coupling, and large hysteresis characteristics of central air conditioning (CAC) system, an energy-saving optimization method based on WTD–CNN–LSTM and multi-strategy improved sparrow search algorithm (MISSA) is proposed, which works to minimize the total energy consumption during the operation of the CAC system. Firstly, minimize the sum of energy consumption of chillers, freezing pumps, cooling pumps, and cooling towers as the objective function, and use the range of operating parameters of each equipment as the basic constraint conditions to establish an energy-saving optimization model for the CAC system. Then, WTD–CNN–-LSTM is used to predict the future cooling load, and the predicted results are used as key constraint condition to achieve on-demand cooling. Finally, MISSA is proposed to improve the initialization, update and convergence stages of the CAC system operation parameter optimization process, accurately obtaining the optimal operating parameters. Compared with manual experience, MISSA reduces energy consumption by 15.32 % and improves energy efficiency ratio by 20.76 %. Meanwhile, compared with other optimization algorithms, MISSA reduces energy consumption by 4.85–13.26 % and improves energy efficiency ratio by 6.53–16.33 % after optimizing the CAC system. The experiment verifies that MISSA is more energy efficient when applied to the CAC system and has the advantages of accuracy, fast convergence, strong global search capability, and the ability to jump out of local optimization.

Bald eagle search algorithm for solving a three-dimensional path planning problem.

Three-dimensional path planning refers to determining an optimal path in a three-dimensional space with obstacles, so that the path is as close to the target location as possible, while meeting some other constraints, including distance, altitude, threat area, flight time, energy consumption, and so on. Although the bald eagle search algorithm has the characteristics of simplicity, few control parameters, and strong global search capabilities, it has not yet been applied to complex three-dimensional path planning problems. In order to broaden the application scenarios and scope of the algorithm and solve the path planning problem in three-dimensional space, we present a study where five three-dimensional geographical environments are simulated to represent real-life unmanned aerial vehicles flying scenarios. These maps effectively test the algorithm's ability to handle various terrains, including extreme environments. The experimental results have verified the excellent performance of the BES algorithm, which can quickly, stably, and effectively solve complex three-dimensional path planning problems, making it highly competitive in this field.

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design.

Neural architecture search (NAS) has attracted much attention in recent years. It automates the neural network construction for different tasks, which is traditionally addressed manually. In the literature, evolutionary optimization (EO) has been proposed for NAS due to its strong global search capability. However, despite the success enjoyed by EO, it is worth noting that existing EO algorithms for NAS are often very computationally expensive, which makes these algorithms unpractical in reality. Keeping this in mind, in this article, we propose an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search. In contrast to existing EO algorithms for CNN architecture design, a new cell-based architecture search space, and new global and local search operators are proposed for CNN architecture search. To further improve the efficiency of our proposed algorithm, we develop a one-epoch-based performance estimation strategy without any pretrained models to evaluate each found architecture on the training datasets. To investigate the performance of the proposed method, comprehensive empirical studies are conducted against 34 state-of-the-art peer algorithms, including manual algorithms, reinforcement learning (RL) algorithms, gradient-based algorithms, and evolutionary algorithms (EAs), on widely used CIFAR10 and CIFAR100 datasets. The obtained results confirmed the efficacy of the proposed approach for automated CNN architecture design.

Visualization design of talent training paths in university education based on pattern knowledge

Abstract In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.

An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final assembly workshop. This is achieved by establishing a multi-objective mixed-integer optimization scheduling model for PBS and solving the model using a state transition algorithm based on the greedy strategy; finally, a comparison was made with the classical genetic algorithm. The results show that the optimal objective function is achieved when the body sequence movement iterations for data sets N and R are 34 and 54, respectively. At this point, the highest objective function scores are 23.46 and 49.79, and vehicles entering the PBS scheduling system from the painting-to-assembly line require 31 and 38 sequence adjustments, respectively. Compared with the classical genetic algorithm, the simulation results show that the state transition algorithm based on the greedy strategy is significantly better; it can also perform well in discrete sequence movement operations, demonstrating strong global search capabilities and fast convergence. It provides a new approach for optimizing vehicle scheduling in the automotive painting-to-assembly line.

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. It is known that image reconstruction of EIT is mathematically an ill-posed inverse problem that leads to poor reconstruction quality. To improve the quality of reconstructed images, a novel image reconstruction method based on modified bird swarm optimization algorithm is proposed in this paper. An image of conductivity distribution acquired by Newton's one-step error reconstruction algorithm performs as initial population. Then a modified bird swarm optimization algorithm is proposed to optimize the initial conductivity distribution. By introducing a dynamic inertia weight parameter, flight interval of the bird swarm varies with the number of iterations. With the proposed method, a strong global search capability in the initial stage and a strong local search capability in the later stage are ensured during the optimization. To verify the effectiveness of the proposed method, simulation work and experimental validation are carried out. Also, reconstructed images are compared with the results obtained by iterative Landweber method and Newton–Raphson method. Meanwhile, the impact of noise on the reconstruction is investigated. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm

The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging.