Global Optimization Research Articles

Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.

Artificial electric field algorithm with repulsion mechanism

AbstractDue to its outstanding performance in addressing optimization problems, artificial electric field (AEF) algorithm has garnered increasing notice in recent years. Nevertheless, numerous studies indicate that AEF is susceptible to premature convergence when the region influenced by the global optimum constitutes a small fraction of the entire solution space. By conducting micro‐level research on the particles during the evolution process of AEF, it is revealed that the primary factors influencing optimization performance are the Coulomb's electrostatic force mechanism and the fixed attenuation factor. Inspired by this observation, we propose an improved version named artificial electric field algorithm with repulsion mechanism (RMAEF). Specifically, in RMAEF, a repulsion mechanism is incorporated to make particles escape from local optima. Furthermore, an adaptive attenuation factor is employed to update dynamically Coulomb's constant. RMAEF is compared with AEF and its state‐of‐art variants under 44 test functions from CEC 2005 and CEC 2014 test suites. From the experiment results, it is obvious that among 14 benchmark functions from CEC 2005 on 30D and 50D optimization, the RMAEF algorithm exhibits superior performance on 8 and 9 functions compared with advanced variants of AEF. For CEC 2014 on 30D and 50D optimization, the RMAEF algorithm produces the best results on 11 and 12 functions, respectively. In addition, three real‐world problems are also used to verify the versatility and robustness. The results demonstrate that RMAEF outperforms its competitors in terms of overall performance.

Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

PGA: A new particle swarm optimization algorithm based on genetic operators for the global optimization of clusters.

We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au20 and double-ring tubular B20, and identifying the ground state clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of (n = 3-30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium-sized 21-30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground-state structures of these (n = 3-30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au20, B20, , and (n = 3-30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request.

A pointwise weighting prediction variance–high-dimensional model representation model-based global optimization approach for ship hull parametric design

Ship hull optimization techniques based on computer-aided design/computational fluid dynamics can effectively enhance the efficiency and stability of ship designs, with significant application prospects. To enhance the potential of ship hull optimization, increasing design variable dimensionality is essential, but can cause a significant increase in hydrodynamic simulations. To reduce simulations required for high-dimensional ship hull optimization, a new surrogate method, pointwise weighting prediction variance–high-dimensional model representation (PWPV-HDMR), which uses pointwise weighting prediction variance (PWPV) to aggregate different a priori assumptions, is developed. Moreover, a differential evolution algorithm is used to identify promising hull design parameters, using the PWPV-HDMR model instead of costly simulation as the fitness function. The proposed approach is tested on the hydrostatic resistance optimization of KRISO Container Ship. The results show that PWPV-HDMR outperforms kriging-HDMR, with a better resistance optimization effect, illustrating the effectiveness of the PWPV-HDMR-based global optimization approach in discovering promising ship hull parameters.

Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems.

In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search operator in SMA to ensure that the agents can evolve in a better direction during the optimization process. Secondly, the best agent is further modified with an improved non-monopoly search mechanism to boost the algorithm's exploitation and exploration capabilities. Finally, a random differential restart mechanism is developed to assist SMA in escaping from local optimality and increasing population diversity when it is stalled. The impacts of three strategies are discussed, and the performance of EMSMA is evaluated on the CEC2017 suite and CEC2022 test suite. The numerical and statistical results show that EMSMA has excellent performance on both test suites and is superior to the SMA variants such as DTSMA, ISMA, AOSMA, LSMA, ESMA, and MSMA in terms of convergence accuracy, convergence speed, and stability.

HAPI-DE: Differential evolution with hierarchical archive based mutation strategy and promising information

Differential Evolution (DE), as a population-based meta-heuristic global optimization technique, has shown excellent performance in handling optimization problems in continuous spaces. Despite its effectiveness, the DE algorithm suffers from shortcomings such as complexity of parameter selection and limitations of the mutation strategy. Therefore, this paper presents a new strategy for generating trial vectors based on a hierarchical archive, which integrates promising information during evolution with current populations to obtain a good perception of the objective landscape. Moreover, to mitigate mis-scaling by scale factor, an adaptive parameter generation mechanism with hierarchical selection (APSH) is proposed. Furthermore, a novel population diversity metric technique and a restart mechanism based on wavelet functions is introduced in this paper. Comparative experiments were conducted to evaluate the performance of the proposed algorithm using 100 benchmark functions from the CEC2013, CEC2014, CEC2017, and CEC2022 test suites. The results demonstrate that the HAPI-DE algorithm outperforms or is on par with 6 recent powerful DE variants. Additionally, HAPI-DE was utilized in parameter extraction for the photovoltaic model STP6-120/36. The findings suggest that our algorithm, HAPI-DE, demonstrates competitiveness when compared to the 6 other DE variants.

Hybrid optimization algorithm based on chaos game and differential evolution for constrained optimization of structures

ABSTRACT A novel hybrid optimization algorithm, chaos game optimization–differential evolution (CGO-DE), is developed in this study for the size optimization of structures. The key contribution lies in the synergistic combination of chaos game optimization (CGO) and differential evolution (DE), using their respective strengths in exploration and exploitation to effectively identify global optima. The innovative approach independently applies CGO and DE to subpopulations, preventing premature convergence and enhancing overall performance while ensuring accuracy, accounting for design factors and respecting constraints. Comprehensive numerical evaluations on established structural benchmarks (three-bar truss, welded beam and cantilever beam) and large-scale structures (272-bar and 942-bar truss tower) demonstrate the versatility and efficiency of CGO-DE in solving complex optimization problems across various engineering applications. The results underscore the ability of the algorithm to achieve optimal solutions and improve designs compared to other optimization techniques, highlighting its potential as a powerful tool for engineering optimization.

An automated framework for material property calibration in loudspeaker simulation model

The shift to virtual meetings, online classes, and remote work has established a new norm, leading to a surge in the use of virtual communication platforms such as Zoom and Microsoft Teams. This shift has increased the demand for high-quality headsets and speakerphones, emphasizing the need for clear, superior audio quality. The process of calibrating material properties typically relies on repetitive simulations guided by experts' intuition, presenting challenges in establishing new Finite Element Models (FEMs) of loudspeakers, as it requires the repeated identification of material property values. We present a systematic framework for calibrating the mechanical material properties of loudspeaker drivers, a crucial prerequisite for developing accurate FEMs of loudspeakers. Specifically, we propose a statistically-driven approach to replace the conventional manual calibration process, which typically relies on multiple simulations guided by expert intuition. Efficient Global Optimization (EGO) is applied to address the expensive optimization problems of loudspeaker simulation. To tackle the curse of dimensionality, the objective function is decomposed into several functions based on effective parameter groups using Global Sensitivity Analysis (GSA) results. The parameters of the FEM are then calibrated to the reference data from the Lumped Parameter Model (LPM) using the decomposed-reduced objective function, providing the calibrated parameters for the loudspeaker simulation. By implementing this novel approach, even individuals without prior knowledge or experience in loudspeaker material properties can effectively and reliably obtain the necessary data for finite element modeling.

Flexible technique for global calibration of multi-view stereo vision with a non-overlapping FOV using linear structured light projector

A flexible global calibration method for multi-view stereo vision with non-overlapping field of view (FOV) using linear structured light projector is presented in this paper. At least three groups of linear structured light plane equations are utilized to calculate the rotation between any pair of cameras with non-overlapping FOV, and then the closed solution of the translation is determined based on the constraint that the normal vector is perpendicular to the lines on linear structured light plane. Once the rotation and translation between all pairs of cameras with non-overlapping FOV are obtained, the rotation and translation of each camera relative to the reference position is eventually computed through global optimization. The experimental data reflects that the calibration accuracy is improved by 35.2% after global optimization, and the global calibration results of the proposed method deviates less from that of Zhang’s method.

The role of point defect reconstructions and polycyclic aromatic hydrocarbons in silicate dust preservation

ABSTRACT Forsterite is a primary constituent of interstellar dust and planetary systems. It is believed to originate from the outflows of oxygen-rich stars and undergo further processing within the interstellar medium through the action of cosmic rays and shocks. Under these harsh conditions, point defects may form, such as MgO Schottky vacancies. These vacancies can then undergo atom reconstruction as part of a chemical process to maintain the system’s crystalline structure. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitously observed interstellar molecules and are thought to form through gas-phase reactions akin to sooting flames. However, their role and impact on dust stability remain unknown. In this study, we employ an atomistic artificial-intelligence-based method, surrogate machine learning trained directly by density functional theory. Specifically, we utilize gofee (global optimization with first-principles energy expressions) to predict possible reconstructions of MgO vacancies on a crystalline forsterite (010) surface as an important component of interstellar dust and planetary systems. We identify nine possible reconstructions involving the formation of unbound Si and O atoms. We investigate their energy stability and find that the reconstruction of Si–O atoms stabilizes the vacancy by about 0.54 eV. Additionally, if PAHs bind with the unbound O and Si atoms of the vacancy, the vacancy is stabilized by approximately 1.76 eV. We demonstrate that PAHs, along with the reconstruction of unbound atoms on the dust surface, affect the stability of the dust, which might open up avenues for diverse chemistry.

A class of one-parameter filled functions with the same local minima as the objective function for global optimization problems

As an effective global optimization method, the filled function algorithm obtains the optimal solution of optimization problems by alternately minimizing the objective function and filled function. One development direction of the filled function method is to construct the filled function with good properties, which directly affects the efficiency of the algorithm and has attracted the attention of scholars. This paper presents a class of one-parameter filled function that is easy to adjust. It is proved theoretically that the filled function is continuously differentiable and has the same local minimizers as the objective function, and these minimizers are all better than the current local minimizer of the objective function. Meanwhile, some natures of the constructed filled function are conducted as followed by a new algorithm is presented. The new filled function algorithm optimizes the iterative framework of the conventional filled function method, improving the efficiency and decreasing the computational cost. The numerical experiments of the new algorithm on several optimization issues are reported with satisfactory computational results, verifying the feasibility and efficiency of the algorithm.

A Power Optimization Approach for Large-scale RM-TB Dual Logic Circuits Based on an Adaptive Multi-Task Intelligent Algorithm

Logic synthesis is a crucial step in integrated circuit design, and power optimization is an indispensable part of this process. However, power optimization for large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits is an NP-hard problem. In this article, we divide Boolean circuits into small-scale circuits based on the idea of divide and conquer using the proposed Dynamic Adaptive Grouping Strategy (DAGS) and the proposed circuit decomposition model (CDM). Each small-scale Boolean circuit is transformed into an MPRM logic circuit by a polarity transformation algorithm. Based on the gate-level integration, we integrate small-scale circuits into an MPRM and Boolean Dual Logic (RBDL) circuit. Furthermore, the power optimization problem of RBDL circuits is a multi-task, multi-extremal, high-dimensional combinatorial optimization problem, for which we propose an Adaptive Multi-task Intelligent Algorithm (AMIA), which includes global task optimization, population reproduction, valuable knowledge transfer (VKT), and local exploration to search for the lowest power for RBDL circuits. Moreover, based on the proposed Fast Power Decomposition Algorithm (FPDA), we proposed a Power Optimization Approach (POA) for an RBDL circuit with the lowest power using the AMIA. Experimental results based on Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the POA compared to state-of-the-art POAes.

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping.

Cation ordering in multication perovskites is related to many important material properties and performances, but computational determination of the cation ordering remains a major challenge. Here, we propose a new computational approach by introducing a machine learning recommender system into the basin-hopping framework (RBH) for optimizing cation ordering. Taking the electrocatalyst Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF5582) as a showcase example, we found that the efficiency of RBH in identifying low-energy configurations outperforms the methods of cluster expansion and conventional basin-hopping. The RBH results revealed that the BSCF5582 catalyst tended to have a layered ordering of A-site cations and disordered B-site cations both in bulk and on the surfaces. Further, on the A-site-terminated surface, we found the segregation of large Ba atoms. Similarly, on the A-site- terminated surface of the recently developed Cs0.2Sr0.8Co0.4Fe0.6O3 (CSCF2846) catalyst, layered ordering at the A-site and surface enrichment of large Cs atoms were also found. The layered ordering was robust against thermal effects, as found from molecular dynamics simulations at 800 K. This work provides a new approach for thermodynamic global optimization of chemical ordering in multicomponent materials.

A two-phase sequential algorithm for global optimization of the standard quadratic programming problem

AbstractWe introduce a new sequential algorithm for the Standard Quadratic Programming Problem (StQP), which exploits a formulation of StQP as a Linear Program with Linear Complementarity Constraints (LPLCC). The algorithm is finite and guarantees at least in theory a $$\delta $$ δ -approximate global minimum for an arbitrary small $$\delta $$ δ , which is a global minimum in practice. The sequential algorithm has two phases. In Phase 1, Stationary Points (SP) with strictly decreasing objective function values are computed. Phase 2 is designed for giving a certificate of global optimality for the last SP computed in Phase 1. Two different Nonlinear Programming Formulations for LPLCC are proposed for each one of these phases, which are solved by efficient enumerative algorithms. New procedures for computing a lower bound for StQP are also proposed, which are easy to implement and give tight bounds in general. Computational experiments with a number of test problems from known sources indicate that the two-phase sequential algorithm is, in general, efficient in practice. Furthermore, the algorithm seems to be an efficient way to study the copositivity of a matrix by exploiting an StQP with this matrix.

Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach

An efficient energy management system (EMS) enhances microgrid performance in terms of stability, safety, and economy. Traditional centralized or decentralized energy management systems are unable to meet the increasing demands for autonomous decision-making, privacy protection, global optimization, and rapid collaboration simultaneously. This paper proposes a hierarchical multi-layer EMS for microgrid, comprising supply layer, demand layer, and neutral scheduling layer. Additionally, common mathematical optimization methods struggle with microgrid scheduling decision problem due to challenges in mechanism modeling, supply–demand uncertainty, and high real-time and autonomy requirements. Therefore, an improved proximal policy optimization (PPO) approach is proposed for the multi-layer EMS. Specifically, in the centrally managed supply layer, a centralized PPO algorithm is utilized to determine the optimal power generation strategy. In the decentralized demand layer, an auction market is established, and multi-agent proximal policy optimization (MAPPO) algorithm with an action-guidance-based mechanism is employed for each consumer, to implement individual auction strategy. The neutral scheduling layer interacts with other layers, manages information, and protects participant privacy. Numerical results validate the effectiveness of the proposed multi-layer EMS framework and the PPO-based optimization methods.

Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems.

Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm's global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications.

Hybrid model-free control based on deep reinforcement learning: An energy-efficient operation strategy for HVAC systems

At present, most optimization objectives of heating, ventilation, and air-conditioning (HVAC) systems focus on the local optimization of equipment during cooling hours, usually ignoring the importance of the end conditions. In addition, traditional control methods may not perform well in complex or dynamic systems. Therefore, in this study, a hybrid model-free control (HMFC) strategy was developed that combines deep reinforcement learning (DRL) with L-BFGS-B and expert knowledge to improve its ability to cope with complex system environments. This strategy was used to optimize the cooling and heating periods of a groundwater source heat pump system in a bitterly cold region of China throughout the year, considering the end-of-system conditions. L-BFGS-B optimized the fresh-air ratios for the end air-handling unit, whereas DRL achieved global optimization of the heat pump outlet temperature, air-conditioning water circulation pump frequency, and submersible-pump frequency. To verify the effectiveness of the strategy, a simulation platform was built based on actual data and device parameters, and the simulation results of HMFC and model predictive control (MPC) were output, compared, and analyzed with data of the system measured in 2022 under expert rule-based manual control (RBC). The results show that HMFC was 7.38 % and 9.38 % more energy efficient than RBC during heating hours, respectively. HMFC was 24.26 % more energy efficient than RBC and 10.03 % more energy efficient than MPC during cooling hours. Moreover, the distribution of the system coefficient of performance under the HMFC method was more concentrated in the higher range, indicating that the proposed HMFC can save energy savings. Thus, it is a feasible optimization scheme for buildings without a large number of historical data. Finally, the strategy was applied to a real engineering experimental analysis, and the engineering practice results show that the strategy is robust and practical.

Ultrasonic signal classification for composite materials via deep convolutional neural networks

ABSTRACT Ultrasonic testing (UT) is the most commonly used non-destructive testing method in the aerospace field. However, for the detection of delamination defect in multi-layer composite adhesive materials with metal and non-metal bonding, traditional energy statistics methods are limited and cannot achieve high-precision automatic signal classification. This article proposes a signal classification model based on hybrid neural networks. By studying the classification accuracy of convolutional neural networks (CNN) and long short-term memory networks (LSTM) for different types of signals, a relatively balanced classification is achieved, which effectively improves the classification accuracy. By integrating attention mechanisms, the ability of the detection model to identify key features is further enhanced. Experimental results demonstrate that the proposed model achieves an accuracy of 98.57%. Bayesian Optimisation (BO) can effectively and automatically select the optimal hyperparameters, and achieve global optimisation. In the experiment, the accuracy increased by 0.73% compared to the benchmark value. Comparative experiments show that the signal classification model established in this paper has good performance.