Global Optimization Research Articles

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices

Many stellarator coil design problems are plagued by multiple minima, where the locally optimal coil sets can sometimes vary substantially in performance. As a result, solving a coil design problem a single time with a local optimization algorithm is usually insufficient and better optima likely do exist. To address this problem, we propose a global optimization algorithm for the design of stellarator coils and outline how to apply box constraints to the physical positions of the coils. The algorithm has a global exploration phase that searches for interesting regions of design space and is followed by three local optimization algorithms that search in these interesting regions (a ‘global-to-local’ approach). The first local algorithm (phase I), following the globalization phase, is based on near-axis expansions and finds stellarator coils that optimize for quasisymmetry in the neighbourhood of a magnetic axis. The second local algorithm (phase II) takes these coil sets and optimizes them for nested flux surfaces and quasisymmetry on a toroidal volume. The final local algorithm (phase III) polishes these configurations for an accurate approximation of quasisymmetry. Using our global algorithm, we study the trade-off between coil length, aspect ratio, rotational transform and quality of quasi-axisymmetry. The database of stellarators, which comprises approximately 200 000 coil sets, is available online and is called QUASR, for ‘quasi-symmetric stellarator repository’.

A Fluid Dynamic Approach to Model and Optimize Energy Flows in Networked Systems

In this paper, attention is focused on the analysis and optimization of energy flows in networked systems via a fluid-dynamic approach. Considering the real case of an energy hub, the proposed model deals with conservation laws on arcs and linear programming problems at nodes. Optimization of the energy flows is accomplished by considering a cost functional, which estimates a term proportional to the kinetic energy of the overall system in consideration. As the real optimization issue deals with an integral formulation for which precise solutions have to be studied through variational methods, a decentralized approach is considered. First, the functional is optimized for a simple network having a unique node, with an incoming arc and two outgoing ones. The optimization deals with distribution coefficients, and explicit solutions are found. Then, global optimization is obtained via the local optimal parameters at the various nodes of the real system. The obtained results prove the correctness of the proposed approach and show the evident advantages of optimization procedures dealing with variational approaches.

AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization

AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6cm, and the maximum longitudinal deviation is 16cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13cm, the average lateral deviation is less than 7cm, and the standard lateral deviation is less than 8cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

Multi-objective-Global-Optimization-Approach Predicted Quasi-Layered Ternary TiOS Crystals with Promising Photocatalytic Properties

Abstract Titanium dioxide (TiO2) has attracted considerable research attentions for its promising applications in solar cells and photocatalytic devices. However, the intrinsic challenge lies in the relatively low energy conversion efficiency of TiO2, primarily attributed to the substantial band gaps (exceeding 3.0 eV) associated with its rutile and anatase phases. Leveraging multi-objective global optimization, we have identified two quasi-layered ternary Ti-O-S crystals, composed of titanium, oxygen and sulfur. The calculations of formation energy, phonon dispersions, and thermal stability confirm the chemical, dynamical and thermal stability of these newly discovered phases. Employing the state-of-art hybrid density functional approach and many-body perturbation theory (quasiparticle GW approach and Bethe-Salpeter equation), we calculate the optical properties of both the TiOS phases. Significantly, both phases show favorable photocatalytic characteristics, featuring band gaps suitable for visible optical absorption and appropriate band alignments with water for effective charge carrier separation. Therefore, ternary compound TiOS hold the potential for achieving high-efficiency photochemical conversion, showing our multi-objective global optimization provides a new approach for novel environmental and energy materials design with multicomponent compounds.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks. Predictive cruise control (PCC) provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information. In this study, a real-time implementable PCC, which simultaneously optimizes engine torque and gear shifting, is proposed for heavy-duty trucks. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control (MPC), in which the upcoming road elevation information is used. Finding the solution of the nonlinear MPC is time consuming; thus, a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method. Dynamic programming (DP) algorithm, as a global optimization algorithm, is used as a performance benchmark for the proposed solver. Simulation, hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller. The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution, with less than 1% deviation for testing roads. Moreover, the proposed co-optimization controller is implementable in a real-truck, and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9% without compromising the truck’s travel time.

Spherical search algorithm with memory-guided population stage-wise control for bound-constrained global optimization problems

The recently proposed Spherical Search (SS) algorithm replaces the traditional square search pattern with a spherical boundary to provide position-diverse solutions. The algorithm balances its exploration and exploitation performance by utilizing 2 exploration and exploitation sub-populations of equal size. SS has been proven to be highly competitive. However, we observed that when it is used to solve a variety of problems as well as during different searching stages, the fixed sub-population size limits its adaptability and flexibility for achieving continuous exploitation–exploration balance. The balance potential of two operators with distinct characteristics is underdeveloped. As a result, SS and its advanced variants are prone to still easily falling into local optima and lacks certain performance advantages over peer algorithms. In this paper, we further develop SS and propose a memory-guided population stage-wise control strategy based SS, called SSM. By our proposed memory-guided stage-wise evaluation mechanism, SS evaluates the exploitation–exploration balance extent in real time and thus adaptively optimizes and predicts better resource allocation ratio values between its 2 sub-populations and thus achieves significant performance advantages over peer algorithms. The experiments are conducted on 120 benchmark functions and 22 real-world problems, and the results show that SSM significantly outperforms other 13 state-of-the-art evolutionary algorithms. Additionally, we conduct analyses based on method characteristics, convergence process, solution quality robustness testing, population diversity, exploitation and exploration balance, and computational complexity.

Compositional design of multicomponent alloys using reinforcement learning

The design of alloys has typically involved adaptive experimental synthesis and characterization guided by machine learning models fitted to available data. A bottleneck for sequential design, be it for self-driven or manual synthesis, by Bayesian Global Optimization (BGO) for example, is that the search space becomes intractable as the number of alloy elements and its compositions exceed a threshold. Here we investigate how reinforcement learning (RL) performs in the compositional design of alloys within a closed loop with manual synthesis and characterization. We demonstrate this strategy by designing a phase change multicomponent alloy (Ti27.2Ni47Hf13.8Zr12) with the highest transformation enthalpy (ΔH) -37.1 J/g (-39.0 J/g with further calibration) within the TiNi-based family of alloys from a space of over 2 × 108 candidates, although the initial training is only on a compact dataset of 112 alloys. We show how the training efficiency is increased by employing acquisition functions containing uncertainties, such as expected improvement (EI), as the reward itself. Existing alloy data is often limited, however, if the agent is pretrained on experimental results prior to the training process, it can access regions of higher reward values more frequently. In addition, the experimental feedback enables the agent to gradually explore new regions with higher rewards, compositionally different from the initial dataset. Our approach directly applies to processing conditions where the actions would be performed in a given order. We also compare RL performance to BGO and the genetic algorithm on several test functions to gain insight on their relative strengths in materials design.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity.

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Optimization of 3D printing supply chain in the era of live streaming e-commerce.

This study examines the effects of the rising live streaming e-commerce on the 3DP supply chain, employing system dynamics to develop separate models for pure polymer and polymer-metal mixed printing. The analysis focuses on optimizing the 3DP supply chain configuration. Results indicate that, based solely on printing time, cost, and quality metrics, Corporate-live-3DP services are optimal for live commerce scenarios. However, despite this, Private-live-3DP maintains a substantial consumer base in practice, as evidenced by literature data and case studies. Both models pose significant challenges to conventional supply chains, necessitating adaptation. For Corporate-live-3DP, optimization strategies may include technology advancements, digital transformation, agile manufacturing, global network optimization, innovative management, collaborative R&D, fine-tuned inventory control, quality system upgrades, talent development, and organizational restructuring. Conversely, Private-live-3DP can be optimized through consolidation of private 3D printing resources, demand prediction and order optimization, supply chain collaboration platforms, quality management extensions, inventory strategy adjustments, increased transparency, regulatory compliance, and risk mitigation measures.

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of “computing power", “thermal energy" and “electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

Optimal permeance ratio, flux direction and layer distribution in composite asymmetric membranes composed of sequences of layers obeying real-power flux laws

In this study, the optimal permeance ratio and layers distribution in multilayer membranes and thin films in which each layer obeys the following real-power law: Flux=πi(P1,ini−P2,ini) are determined for any positive real values of ni. Such an exponent has a physical meaning in several cases, such as: diffusion-controlled permeation of hydrogen in metal membranes i) under infinite-dilution conditions (n = 0.5), and ii) under higher-concentration ones (0.5 < n < 1.5), iii) desorption-limited permeation (n = 1) in the same type of membranes, Knudsen-type diffusional mechanism (n = 1) and viscous flow (n = 2) in microporous membranes. Furthermore, n is equal to 4 in radiation-driven thermal flux. The work considers two generic layers of different thicknesses and exponents, for which it is proven the existence of a general optimal permeance ratio π1/π2 maximising the flux ratio, which is found to be equal to the inverse of the ratio between the respective maximum theoretical driving forces that can be established in each layer. Such an optimal permeance ratio is also proven to be a global optimum. Afterwards, we provide a theoretical generalisation of this optimality to whatever types of driving forces. Then, we provide a demonstration that the highest species permeating flux is always established when feeding it from the layer side with the highest exponent independently of the permeance ratio. As an important consequence, to maximise flux, a multilayer membrane/thin film should be fabricated by depositing the layers in cascade according to the ascending or descending exponents, and should be used in a configuration in which the flux flows from the layer with the highest pressure exponent to the layer with the lowest one. It is also shown that there exists an optimal value of the lower exponent n1 that maximizes the flux ratio keeping constant the other parameters. Finally, we showed that a virial-type linear combination of polynomial driving forces can be approximated by the empirical flux law investigated. Overall, our results are valid not only for whatever type of composite membranes and thin films (metallic, ceramic, polymeric and hybrid ones), but also for any sequential system involving a flux of whatever physical entity obeying the aforementioned law, such as heat transfer in the presence of radiation, non-linear electrical resistances and possible others.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

This study intends to construct and optimally run a grid-connected hybrid renewable energy system (HRES) powering a residential building. A special techno-economic multi-objective optimization technique is used for sizing the HRES. In addition, an energy management strategy (EMS) is established as a distinct multi-objective optimization problem to minimize degradation and operating costs while improving system performance. A Bi-level mixed integer non-linear programming (BMINLP) is the framework used to formulate the two-optimization problems. Sizing problem is characterized by the higher level, on contrast; the nested EMS problem within the constraints of the size problem is presented on the lower level. For this investigation, the problem is executed via the MATLAB program. The global optimization toolbox is applied via the Multi-Objective Genetic Algorithm (MOGA) to solve the sizing optimization. While the EMS is executed using the "Intlinprg" mixed integer linear programming (MILP) solver. The technical and economic performance parameters such as degradation, renewable energy fraction (REF), operating, maintenance, and investment costs are evaluated in this study. The primary results reveal that the summer week encounters the lowest total cost and the highest REF, at roughly 99484 $ and 60 %, respectively. Furthermore, the investigation shows that the lowest degradation value attained in the same week with approximately 50.

Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies

Abstract Recent metaheuristic approaches are extensively and intensively being implemented to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a newly established metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After recognizing the modal features of the objective function tailored and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty appraisal tests have been further implemented to comprehend the reliability of solutions obtained. The solutions retrieved by HGS have been unbiasedly compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly used particle swarm optimization algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, stably, robustly, and coherently with the available geophysical, geological, and borehole verification, as HGS is able to better explore the model space without compromising its capability to efficiently approach the global minimum. This novel global optimization method can thus be considered as a promising tool in geothermal energy investigations and the study of volcanic activities.

Shape optimization of annular transonic thrust nozzles via genetic algorithm and adjoint method

In this research, the idea of combining the genetic algorithm and adjoint method was proposed to leverage the benefits of both approaches to improve the accuracy of thrust nozzle shape optimization. Although the genetic algorithm can search the entire design space to approach the global optimum, its computational cost increases considerably with the number of design variables. Meanwhile, the adjoint method is unaffected by the design variables, but it cannot achieve the global optimum directly and relies on the initial guess geometry. To address these challenges, a genetic algorithm was initially employed to approximate the global optimal geometry of a thrust nozzle with a conical center body parameterized by a Bezier curve. A MATLAB code integrated the geometry parameterization, genetic algorithm, and Fluent R2 2021 flow solver. The resulting geometry was then considered as the initial guess for the adjoint method, which accurately achieved the final optimal geometry with increased control points. The genetic algorithm aimed to maximize the thrust coefficient as the objective function, while the adjoint solver aimed to minimize the axial velocity variance at the nozzle outlet as the cost function. Three distinct constraints were incorporated in the optimization process, and the influence of the center body shape on the results was thoroughly examined to consider all possible geometries throughout the optimization procedure. The findings revealed that the conical center body effectively adjusted the flow angle at the exit, significantly affecting the thrust force. Both optimization steps reduced the average velocity and increased the average pressure at the nozzle exit, enhanced the mass flow rate, and finally increased the thrust force. The genetic algorithm and adjoint method contributed to 3 % and 0.6 % increases, respectively, in the thrust force, resulting in a thrust coefficient of 96.15 % for the final optimized nozzle.

Integration of bat algorithm and salp swarm intelligence with stochastic difference variants for global optimization

Integration of bat algorithm and salp swarm intelligence with stochastic difference variants for global optimization

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Obtaining the Global Optimum of an NP-Hard Problem: School Schedule through the Three-Stage Strategy

Pκ-Curves: Interpolatory curves with curvature approximating a parabola

This paper introduces a novel class of fair and interpolatory planar curves called pκ-curves. These curves are comprised of smoothly stitched Bézier curve segments, where the curvature distribution of each segment is made to closely resemble a parabola, resulting in an aesthetically pleasing shape. Moreover, each segment passes through an interpolated point at a parameter where the parabola has an extremum, encouraging the alignment of interpolated points with curvature extrema. To achieve these properties, we tailor an energy functional that guides the optimization process to obtain the desired curve characteristics. Additionally, we develop an efficient algorithm and an initialization method, enabling interactive modeling of the pκ-curves without the need for global optimization. We provide various examples and comparisons with existing state-of-the-art methods to demonstrate the curve modeling capabilities and visually pleasing appearance of pκ-curves.