Global Optimization Algorithm Research Articles

Global optimization for large‐scale water network synthesis based on dynamic partition and adaptive bound tightening

AbstractThe synthesis of large‐scale integrated water networks is typically formulated as nonconvex mixed‐integer quadratic constrained programming (MIQCP) or QCP problems. With the complexity arising from bilinear terms in modeling mass flows of contaminants and binary variables representing the presence of units or streams, numerous local optima exist, thus presenting a significant optimization challenge. This study introduces a deterministic global optimization algorithm based on mixed‐integer programming (MIP) to tackle such problems. The approach involves dynamically strengthening the relaxed problems to converge towards the original problems. A simultaneous partition strategy is proposed combining locally uniform division with dynamic partitioned variables choosing. Furthermore, several adaptive bound contraction schemes are introduced to efficiently manage the size of the relaxed problems, assisting in accelerating the solution process. The algorithm's effectiveness and robustness are demonstrated with a large test set, showing superior performance compared to commercial solvers specifically on MIQCP problems.

Trajectory optimization of near-Earth asteroids exploration by using reusable probes from cislunar space

Implementing the flyby to Near-Earth Asteroids (NEAs) with the potential impact risks to the Earth allows for obtaining detailed physical parameters, thereby supporting the high-precision orbit prediction and planetary defense strategy. Different from those conducted asteroid flyby missions, in the 12th China Trajectory Optimization Competition (CTOC-12), a NEAs flyby trajectory design problem using reusable probes that depart from a Lunar Distant Retrograde Orbit (DRO) station in the cislunar space was released. The objective was flyby to as many NEAs as possible using up to 20 probes within a total of 10 years. The ∑ team proposed a solution that can explore 47 NEAs using 11 probes, ranking the first in the competition. In this paper, the methods and results from the winning team are introduced, including mission analysis and preliminary design, and low-energy transfer trajectory optimization. In particular, a round-trip trajectory is divided into three phases: deep space transfer, indirect transfer between the Earth to DRO, and DRO phasing and rendezvous. With the combination of global optimization and local optimization algorithms, the required velocity increments to change the orbital planes are effectively reduced, thus increasing the number of the explored NEAs. The final solution of our team is presented and the results are compared with those of the top three teams. The competition demonstrates that the regularization of flyby missions from the cislunar space to explore NEAs with the potential impact risks to the Earth is the feasible and promising.

Particle swarm optimization for hybrid mutant slime mold: An efficient algorithm for solving the hyperparameters of adaptive Grey-Markov modified model

Thegrey prediction model is a scientific and effective prediction method for small amounts of incomplete data. In this paper, we proposed a particle swarm optimization for mixed mutant slime mold (namely SCPSO) combined with an extended adaptive Grey-Markov modified model called AOPGM(1,1,λ,µ), which constructs an AOPGM(1,1,λ,µ) based hyperparameters optimization for SCPSO. Firstly, the values of the exponential cumulative generation operator coefficient λ and the background value µ of the adaptive Grey-Markov modified model are refined, and λ and µ are also calculated using SCPSO. Secondly, a competitive SCPSO is formed by introducing a good point set, identifying attack strategy, mutation slime mold algorithm, and Sigmoid function into particle swarm optimization. It was also compared with the improved optimization algorithms on the CEC2020 test set and with classical and newer intelligent algorithms on the CEC2022 test set. The results of numerical experiments show that SCPSO can be considered a competitive and promising global optimization algorithm. Finally, SCPSO is used to optimize the hyperparameters of the AOPGM(1,1,λ,µ) and applied to the prediction of oil production and proven reserves in China. In terms of optimizing the hyperparameters of the AOPGM(1,1,λ,µ), the numerical experimental results of this method were better than the prediction results of 7 intelligent algorithms and the results of 4 prediction models. The validity of the proposed methodology was verified through five evaluation indicators.

An ionic liquid in Core-shell structure: Halogen-free, metal-free bifunctional catalyst for olefin epoxidation and CO2 cycloaddition

We synthesized a core-shell resin structure with abundant architecture and excellent thermal stability through polymerization. Imidazole ionic liquid with varying carbon chain lengths was immobilized on the surface, resulting in the preparation of metal-free, halogen-free core-shell catalysts with different carbon chain lengths via HCO3- exchange. Characterization using FT-IR, XPS, and various techniques revealed the exceptional performance of our synthetic catalyst in terms of its internal structure. After extensive experimentation, we discovered that the synthesized catalyst exhibits dual functionality for epoxidation and CO2 cycloaddition reactions without requiring solvents or co-catalysts. The epoxidation system demonstrated remarkable conversion rates and selectivity while also exhibiting strong recyclability in heterogeneous reactions, according to kinetic parameters, the reaction order of styrene, TBHP and catalyst during epoxidation is approximately 1, the reference factor for this reaction was calculated to be 6.7×109 (L2·mol−2·min−1), with an activation energy of 48.9 kJ/mol obtained from analyzing reaction rates at different temperatures. In the CO2 cycloaddition reaction, our catalyst exhibited an advantage in catalyzing ring-opening reactions, achieving a conversion rate of 95 % for styrene oxide within six hours along with over 99 % selectivity towards cyclic carbonate formation. It is suggested that the epoxide reaction is carried out in steps, and it is inferred that the catalyst PS-ImC4HCO3 and TBHP are produced into peroxy intermediate active species TBA and HCO4, and the reaction between HCO4- and styrene is the determination step of the total reaction. The reaction rate constant k=0.009 of the absolute step is calculated based on the global optimization algorithm

A novel hybrid adaptive differential evolution for global optimization

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems

A new nature-inspired optimization algorithm, Piranha predation optimization algorithm (PPOA), is proposed based on the unique foraging and predation behaviors of piranhas. Briefly, PPOA consists of three optimization operations, i.e., narrowing down to tear prey, swimming in a straight line, and swimming in a spiral. In this paper, various mathematical models for simulating the behavioral operators are presented in detail to solve different optimization challenges effectively. In this paper, the performance of PPOA is rigorously tested on 23 benchmark optimization functions, CEC2017 competition test set, CEC2020 real-world engineering optimization problems and four engineering design applications to show the applicability of the algorithm in different applications. Comparison experiments with other good and advanced competitive algorithms are conducted to reveal the advantages and performance of PPOA by using performance metrics such as Wilcoxon rank sum test and Friedman mean rank. The comparative results of this paper demonstrate the effectiveness of the proposed algorithmic strategy and its potential in applying it to solving optimization real-world engineering optimization problems.

Optimizing Air Contactor Design for Efficient Carbon Capture in Direct Air Capture Systems: A Multi-Disciplinary Approach

As carbon dioxide (CO2) emissions continue to rise, direct air capture (DAC) has emerged as an important technology for removing CO2 from ambient air to combat climate change. The air contactor design is a critical factor impacting DAC system performance and costs. This research describes a strategy for improving the design of a single air contactor unit to increase CO2 capture flux while minimizing costs using optimization techniques grounded in experimental data .The research focuses on a 100 m2 slab air contactor modeled utilizing cooling tower and scrubber principles. The system includes the chemical, mechanical, and structural engineering disciplines. The mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area are four major design characteristics that were maximized.The model integrated requirements for low air velocity, pressure drop, and energy consumption, as well as adequate gas-liquid interaction via structured packing selection and depth.The design space was explored using a multi-objective optimization strategy based on gradient-based and heuristic methods. Initial sampling via a design of experiments methodology demonstrated response surface trends in the objective function across the design space. The response surfaces mapped the optimization objective as a function of the design variables. Using gradient information, sequential quadratic programming efficiently located an optimal design vector that met all constraints on the design variables and feasibility requirements. The constraints included bounds on mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area. The optimized air contactor design achieved a CO2 capture flux of 10,440 kg/m2-yr at a cost of $ 0.068 per kg CO2 captured. The developed optimization framework maximizes contactor performance while decreasing operational costs. A multi-objective genetic algorithm approach was used to identify the trade-offs between the two competing objectives of maximized CO2 capture and minimized cost by expanding the formulation. The Pareto-optimal front of the optimization results offered numerous optimized solutions with specific DAC system setups. This methodology, as well as the optimum configurations identified, serve as the foundation for constructing larger-scale DAC systems, in future. Ongoing research aims to improve solution approaches by including advanced optimization software packages such as DAKOTA, which offers global optimization algorithms, as well as to boost model accuracy via integrating additional choice variables. Future optimization and modeling studies will include contactor interactions and the modeling of a full DAC facility to boost confidence that a globally optimal design has been established.Overall, this study illustrated an efficient optimization strategy for air contactor design and contributes to the improvement of sustainable carbon capture technology, which is crucial to controlling rising global CO2 levels. Contactor configuration optimization enables superior DAC system designs at lower costs, hence promoting the urgent deployment of negative emissions solutions.

Fast high quality highlight removal using a simplified matrix iteration method

Recently, a global non-smooth optimization method was developed for efficiently eliminating specular highlights from single color image. Although this method was reported to be effective on both natural and medical images, its algorithm’s speed is slow due to the non-smoothness of the objective function. This paper proposes a global smooth optimization method, where two gradient regularization terms used to describe diffuse and specular component features for texture details. A new simplified optimality condition is established and thus a matrix iteration algorithm for fast specular highlight removal is proposed. By employing the gradient regularization of diffuse reflection, image texture detail can be preserved during specular highlight removal. Due to the smoothness of the objective function, the proposed matrix iteration algorithm has lower computational complexity and less storage requirement than the global non-smooth optimization algorithm. Furthermore, the proposed matrix iteration algorithm is guaranteed to converge to a global optimal solution under a fixed step length. Experimental results demonstrate that the proposed matrix iteration algorithm is much faster than the non-smooth optimization algorithm. Moreover, the proposed highlight removal algorithm is more effective on benchmark natural images and real laparoscopic images than other highlight removal algorithms.

Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

On Dealing with Minima at the Border of a Simplicial Feasible Area in Simplicial Branch and Bound

AbstractWe consider a simplicial branch and bound Global Optimization algorithm, where the search region is a simplex. Apart from using longest edge bisection, a simplicial partition set can be reduced due to monotonicity of the objective function. If there is a direction in which the objective function is monotone over a simplex, depending on whether the facets that may contain the minimum are at the border of the search region, we can remove the simplex completely, or reduce it to some of its border facets. Our research question deals with finding monotone directions and labeling facets of a simplex as border after longest edge bisection and reduction due to monotonicity. Experimental results are shown over a set of global optimization problems where the feasible set is defined as a simplex, and a global minimum point is located at a face of the simplicial feasible area.

An improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor images

An improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor images

A Method for Air Route Network Planning of Urban Air Mobility

Urban air mobility is an effective solution to address the current issue of ground traffic congestion in future cities. However, as the user scale continues to expand, the current civil aviation flight scheduling and control methods are becoming inadequate to meet the high-volume flight guarantee demands of future urban air transportation. In order to effectively handle and resolve potential issues in this field in the future, this paper proposes a method for planning urban air mobility route networks. The planning process is divided into two stages: construction and optimization. Methods for constructing urban air mobility route networks based on flight routes and global optimization methods based on node movement are proposed in each stage. In the construction stage, a complete construction process is designed to generate routes based on existing flight routes, in line with the trend of urban air transportation development. In the optimization stage, inspired by the ant colony algorithm, node transfer rules and information transfer rules are incorporated to design a global optimization process and algorithm for route networks. Experimental results demonstrate the effectiveness and advancement of the proposed planning method.

Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

Portfolio Selection Based on Mean-Generalized Variance Analysis: Evidence from the G20 Stock Markets

Modern finance theories have been increasingly paying attention to nonlinear and asymmetric features of stock returns. In this paper, we extend the concept of covariance to generalized covariance by using Generalized Measure of Correlation (GMC). Based on the generalized covariance which is capable of catching the nonlinearity and asymmetry in stock (index) returns, we propose a mean-generalized variance portfolio selection model which considers the gross-exposure constraint. Furthermore, we propose the corresponding nonparametric estimation approach and the global optimization algorithm to enhance the applicability of our new model. Empirical studies on G20 stock markets support that a portfolio considering nonlinear and asymmetric features among the international markets would outperform traditional ones based on mean-variance optimization and equal weighting strategy in terms of return and flexibility.

GLIM: 3D range-inertial localization and mapping with GPU-accelerated scan matching factors

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.

Probing the Structural and Electronic Properties of the Anionic and Neutral Tellurium-Doped Boron Clusters TeBnq (n = 3-16, q = 0, -1).

In this study, we employ density functional theory along with the artificial bee colony algorithm for cluster global optimization to explore the low-lying structures of TeBnq (n = 3-16, q = 0, -1). The primary focus is on reporting the structural properties of these clusters. The results reveal a consistent doping pattern of the tellurium atom onto the in-plane edges of planar or quasi-planar boron clusters in the most energetically stable isomers. Additionally, we simulate the photoelectron spectra of the cluster anions. Through relative stability analysis, we identify three clusters with magic numbers -TeB7-, TeB10, and TeB12. The aromaticity of these clusters is elucidated using adaptive natural density partitioning (AdNDP) and magnetic properties analysis. Notably, TeB7- exhibits a perfect σ-π doubly aromatic structure, while TeB12 demonstrates strong island aromaticity. These findings significantly contribute to our understanding of the structural and electronic properties of these clusters.

An Improved Football Team Training Algorithm for Global Optimization.

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.

Improved density prediction model based on global optimization algorithm for GPR system

Density prediction models are key to pavement compaction and density estimation using ground penetrating radar (GPR). However, current models do not account for the effects of dielectric constant errors and rely on limited data for parameter fitting. This article introduces an electromagnetic (EM) mixing theory and genetic algorithm (GA) based density prediction model named EM-GA. A comprehensive density database is established from literature, laboratory and field tests. After comparing Bayesian optimization, GA, simulated annealing (SA), and particle swarm optimization (PSO), the GA, which performs the best, is selected. On the testing set, the EM-GA model outperforms traditional models, with a notable reduction of 43.6 % in Mean Absolute Percentage Error (MAPE) and 39.8 % in Root Mean Square Error (RMSE). More importantly, the EM-GA predictions are accurate in the validation of four continuous survey lines. The proposed EM-GA model effectively enhances the accuracy of GPR-based density estimation for newly constructed asphalt pavement.

Global optimization in variational quantum algorithms via dynamic tunneling method

We present a global optimization routine for the variational quantum algorithms, which utilizes the dynamic tunneling flow. Originally designed to leverage information gathered by a gradient-based optimizer around local minima, we adapt the conventional dynamic tunneling flow to exploit the distance measure of quantum states, resolving issues of extrinsic degeneracy arising from the parametrization of quantum states. Our global optimization algorithm is applied to the variational quantum eigensolver for the transverse-field Ising model to demonstrate the performance of our routine while comparing it with the conventional dynamic tunneling method, which is based on the Euclidean distance measure on the parameter space.

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic. Keywords: optimization, swarm intelligence, mathematical modelling, nonlinear regression, complex objects and systems.