Global Solution Research Articles

Multi-modal Battle Royale optimizer

AbstractMultimodal optimization poses a challenging problem in the field of optimization as it entails the discovery of multiple local and global optima, unlike unimodal optimization, which seeks a single global solution. In recent years, the significance of addressing multimodal optimization challenges has grown due to the real-world complexity of many problems. While numerous optimization methods are available for unimodal problems, multimodal optimization techniques have garnered increased attention. However, these approaches often grapple with a common issue: the determination of the niching parameter, necessitating prior knowledge of the problem space. This paper introduces a novel multimodal optimization approach that circumvents the need for prior problem space knowledge and avoids the challenge of predefining the niching parameter. Building upon the Battle Royal Optimization (BRO) algorithm, this extended version formulates a multimodal solution by utilizing Coulomb's law to identify suitable neighbors. The incorporation of Coulomb's law serves the dual purpose of identifying potential local and global optima based on fitness values and establishing optimal distances from solution candidates. A comparison study was done between the MBRO and seven well-known multimodal optimization algorithms using 14 benchmark problems from the CEC 2013 and CEC 2015 competitions to see how well it worked. The experimental results underscore MBRO's proficiency in successfully identifying most, if not all, local and global optima, positioning it as a superior solution when compared to its competitors.

Global Weak Solution for Phase Transition Equations with Polarization

This paper deals with a phase transition model with polarization which describes the thermodynamic, electromagnetic, and polarization properties of ferromagnetic–ferroelectric materials. The existence of the global weak solution for the phase transition equations with polarization is rigorously established through the viscosity vanishing argument.

A matrix-form complex variable method for multiple non-circular tunnels in layered media

This paper presents a matrix-form complex variable method that can predict the stress and displacement field around multiple tunnels in layered media. The proposed matrix-form method avoids complicated coefficient determinations and iterative algorithm, which effectively improves the computation efficiency. Firstly, the expressions for the stress and displacement field are given under the generalized complex variable theory. The conformal mapping technique and fast Fourier transform algorithm are used to formulate the matrix-form expressions of boundary conditions. Considering the boundary conditions of the tunnels and soil interfaces, the global matrix solution for multiple tunnels in layered media are obtained by assembling the matrix solutions of all single layers. The present theory is verified by comparison with the existing literature, finite element analysis and field data in engineering practices, and several numerical examples are given to reveal the effects of elastic properties, tunnels' position, equivalent layer and the layer thickness surrounding tunnels.

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR.

Forwards Attractor Structures in a Planar Cooperative Non-autonomous Lotka–Volterra System

The global attractor of a dissipative dynamical system provides the necessary information to understand the asymptotic dynamics of all the system’s solutions. A crucial question consists in finding the structure of this set. In this paper we provide a full characterization of the structure of attractors for a planar non-autonomous Lotka–Volterra cooperative system. We show sufficient conditions for the existence of forward attractors and give a full description of them by proving the existence of such bounded global solutions that all bounded global solutions join them, i.e. converge towards them when time tends to plus and minus infinity. These results generalize those known in an autonomous framework. The case of particular interest in our work is the situation where globally forward-stable non-autonomous solutions have both coordinates strictly positive. We study this case in detail and obtain sufficient conditions that the problem parameters must satisfy in order to obtain various structures of non-autonomous attractors. This allows us to understand different paths of the solutions towards the unique globally stable one.

Market Trends and Outlook for Oil and Gas, a Review

Hydrocarbon business globally, commands enormous influence over the dynamics of financial integrity on wealth creation in the world economy, through global trade. Citing example with Nigeria’s Oil and Gas industry, her richly endowed mineral deposits, for the past six decades has (directly or indirectly) played very significant role on virtually all economic and commercial activities; in determining the financial health/viability of the nation’s economy. The global economy presently faces challenges of the global warming due to the climate change that has occurred as a result of incremental growth in energy demands and consumption. Energy, known as a critical factor to life is essential for purposes of powering the economy through daily economic activities, and also for maintenance of man’s existence, has its own consequences on the environment in the course of its production and utilization. As a result, it has been proven to be posing a number of challenges, threats to life and man’s sustainable existence because of the adverse incidences on the planet, resulting from impact of carbon emissions being generated from energy consumption of fossil fuels/Oil and Gas sources (among other greenhouse gases, GHGs). Energy transition, as a phenomenal trend on the global energy solutions management, would contain these global challenges (in ever increasing global population with its imminent threat of energy crisis/poverty) by applying energy-mix menu and technologically advanced initiatives (the proposed Maximum energy-minimum emissions strategy) to control global warming by net-zero carbon emissions target through decarbonizing technologies; for sustainability of the planet.

Prediction of Tensile Properties in Inconel 625 Superalloy Fabricated by Wire Arc Additive Manufacturing Using Improved Artificial Neural Network

This study exhibits the possibility of using an artificial neural network (ANN) to model the mechanical behavior of wire arc additive manufacturing (WAAM) for Inconel 625. For this reason, tensile tests of Inconel 625 superalloy as-built (AB) samples and samples after heat treatment at 1200 °C (HT-1200) by WAAM were performed. For the HT-1200 samples, the yield stress decreased, and the elongation increased significantly due to grain refinement and the formation of annealed twins. A new hybrid model combining a swarm intelligence optimization algorithm with a back propagation neural network (BPNN) was developed to simulate the flow behavior of the superalloy. Compared with other hybrid BPNN models that have been reported, the proposed BPNN model is in better agreement with the experimental data and provides a better description of the flow stress of the Inconel 625 superalloy. The excellent predictive ability of the model may be attributed to the optimization of the weights and thresholds of the BPNN, which obtains the optimal global solution in the search space more efficiently.

Global solutions to the Kirchhoff equation with spectral gap data in the energy space

We prove that the classical hyperbolic Kirchhoff equation admits global-in-time solutions for some classes of initial data in the energy space. We also show that there are enough such solutions so that every initial datum in the energy space is the sum of two initial data for which a global-in-time solution exists. The proof relies on the notion of spectral gap data, namely initial data whose components vanish for large intervals of frequencies. We do not pass through the linearized equation, because it is not well-posed at this low level of regularity.

Impact of incorporating Spire CubeSat GPS observations in a global GPS network solution

Commonly, Global Positioning System (GPS)-based precise orbit determination (POD) of a low Earth orbiting (LEO) satellite is conducted by introducing fixed GPS orbits and clock corrections that were derived in a previous independent network solution using ground-based GPS receivers only. There have been a number of studies showing that the integration of LEO GPS observations can be advantageous in global network solutions, particularly when it comes to estimating geodetic parameters, such as the Earth’s center-of-mass. There has been an increase in GPS data available from LEO CubeSats over the past few years, such as from the Spire satellites, which are equipped with dual-frequency GPS receivers. The goal of the present study is to determine how GPS observations collected by specific Spire satellites affect global network solutions when combined with GPS observations from ground stations of the International GNSS Service. To obtain a combined GPS-LEO solution, GPS observations collected from receivers of selected Spire satellites and additional scientific LEOs are used to obtain combined GPS-LEO solutions by performing one joint least-squares adjustment process. Using this method, LEO satellite orbit parameters are determined along with GPS orbit parameters and geodetic parameters, namely GNSS station coordinates, Earth rotation parameters (ERPs), and the Earth’s center-of-mass. The influence of the Spire LEOs is assessed by analyzing different solutions that use GPS observations from different Spire and LEO satellites. Besides comparing the different global network solutions with one another, a comparison to a solution using only terrestrial GPS data is performed as well. As part of the quality assessment of the GPS-LEO solution, quality characteristics are analyzed, which are the geodetic parameters, orbit overlaps at arc boundaries, and formal errors determined for the estimated parameters. The results show that the estimation of the Earth’s center-of-mass coordinates benefits from including GPS-LEO observations. The solution is more improved by integrating GPS-LEO observations from scientific LEOs than from GPS-Spire-included solutions. The analysis reveals that GPS orbits are improved using the LEO-integrated approach, while orbit misclosures of LEO and Spire trajectories currently indicate a decrease in LEO orbit precision due to LEO orbit modeling deficiencies.

Boundedness of Solutions for an Attraction–Repulsion Model with Indirect Signal Production

In this paper, we consider the following two-dimensional chemotaxis system of attraction–repulsion with indirect signal production 𝜕tu=Δu−∇·χ1u∇v1+∇·(χ2u∇v2),x∈R2,t>0,0=Δvj−λjvj+w,x∈R2,t>0,(j=1,2),𝜕tw+δw=u,x∈R2,t>0,u(0,x)=u0(x),w(0,x)=w0(x),x∈R2, where the parameters χi≥0, λi>0(i=1,2) and non-negative initial data (u0(x),w0(x))∈L1(R2)∩L∞(R2). We prove the global bounded solution exists when the attraction is more dominant than the repulsion in the case of χ1≥χ2. At the same time, we propose that when the radial solution satisfies χ1−χ2≤2πδ∥u0∥L1(R2)+∥w0∥L1(R2), the global solution is bounded. During the proof process, we found that adding indirect signals can constrict the blow-up of the global solution.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

Global Solution and Stability of a Haptotaxis Mathematical Model for Complex MAP

A critical function of polymeric matrices in biological systems is to exert selective control over the transport of thousands of nanoparticulate species. Utilizing “third-party” molecular anchors to crosslink nanoparticulates to the matrix is an effective strategy, and a trapped nanoparticulate formed a desired complex MAP that is necessary to keep the nanoparticulate immobilized at any given time. In this paper, the global solution and stability of a parabolic–ordinary-parabolic haptotaxis system to complex MAP are studied. First, the existence of a local classical solution to system () has been observed using fixed point argument and parabolic Schauder estimates. Furthermore, some a priori estimates that can raise the regularity estimate of the solution for the relatively complicated first equation of system () from Lρ to L2ρ (ρ≥1) are given; then, the local classic solution can thus extend to the global classic solution when the space dimension N≤3. Lastly, by using various analytical methods, a threshold value ξ00(ξ00<0) is found, such that positive constant steady state (u*,v*,w*) becomes unstable when ξ<ξ00. Our results show that the haptotaxis plays a crucial role in determining the stability to the model (), that is, it can have a destabilizing effect.

Vanishing viscosity limit for incompressible axisymmetric flow in the exterior of a cylinder

In this paper, we study the initial boundary value problem and vanishing viscosity limit for incompressible axisymmetric Navier–Stokes equations with swirls in the exterior of a cylinder under Navier-slip boundary condition. In the first part, we prove the existence of a unique global solution with the axisymmetric initial data u0ν∈Lσ2(Ω) and axisymmetric force f∈L2([0,T];L2(Ω)) . This result improves the initial regularity condition on the global well-posedness result obtained by K Abe and G Seregin (2020 Proc. R. Soc. Edinburgh A 150 1671–98) and extends their boundary condition. In the second part, we make the first attempt to investigate the inviscid limit of unforced viscous axisymmetric flows with swirls and prove that the viscous axisymmetric flows with swirls converge to inviscid axisymmetric flows without swirls under the condition ‖ru0θν‖L2(Ω)=O(ν) . Some new uniform estimates, independent of the viscosity, are obtained here. The second result can be thought as a follow-up work to the previous work by K Abe (2020 J. Math. Pures Appl. 137 1–32), where the inviscid limit for the same equations without swirls in an infinite cylinder was studied.

Optimal Design of a Hybrid Solar–Battery–Diesel System: A Case Study of Galapagos Islands

In this study, the sizing problem of hybrid diesel–photovoltaic–battery systems was determined using a particle swarm optimization approach. The goal was to optimize the number of solar panels and batteries that could be installed to reduce the overall cost of an isolated grid system, originally powered by diesel generators, located on Isabela Island in the Galapagos, Ecuador. In this study, real solar radiation and temperature profiles were used, as well as the load demand and electrical distribution system relative to this island. The results reveal that the total cost for the proposed approach is lower as it reaches the global optimal solution. It also highlights the advantage of a hybrid diesel–photovoltaic–battery (DG-PV-BAT) system compared to conventional systems operated exclusively by diesel generators (DGs) and systems made up of DGs and PV panels; compared to them, a reduction in diesel consumption and total cost (71% and 56%, respectively) is achieved. The DG-PV-BAT system also considerably improves environmental factors and the quality of the power line. This study demonstrates the advantages of hybridizing systems isolated from the network through the proposed approach.

NGS: A network GPGPU system for orchestrating remote and virtual accelerators

In General-Purpose computing on Graphics Processing Unit (GPGPU), the use of CPUs is combined with that of GPUs. CPUs are used for sequential code, while GPUs are used for parallel code. GPGPU has been enabled by two key factors: (i) the massively parallel architecture of GPUs, which allows thousands of single cores to run parallel code; and (ii) the development of platforms, such as CUDA, that simplify implementing code for GPUs. GPGPU has established itself as the standard computing system in most computing fields due to the great improvements it brings. However, its use is not without problems, such as GPU underutilization, high cost, power consumption, etc. In this paper we present NGS (Network GPGPU System) to address the underutilization of GPUs in computing centers. NGS orchestrates the concurrent access to GPGPU resources from different nodes of the cluster by leveraging the remote GPU virtualization mechanism and the NVML library by NVIDIA. In this way, NGS enables different nodes of the cluster to access remote GPUs as if they were local at the same time that this access is guaranteed to be carried out without collisions. The main novelty is that NGS offers a global and standard solution independent of the computing environment used. Experimental results show up to 4x improvements compared to popular approaches.

Numerical Analysis of the Co-firing Combustion of Coal and Palm Shell Kernel In Stoker Boiler

The United Nations Framework Convention on Climate Change (UNFCCC) states that Indonesia is committed to contributing to global climate change solutions. The government will also continue to encourage the development of a number of renewable energy (EBT)-based power generation projects. This is based on the use of renewable energy which is still low, namely around (1.9%) 8215.5 MW. Meanwhile, the potential for EBT to become energy can be around 443,208 MW. One source of EBT in Indonesia that can be utilized is Biomass. Co-firing of biomass is a relatively cheaper option and does not require investment in new power plants. However, Co-firing combustion has several aspects that need to be studied, such as temperature combustion and the generation of gas emissions. Therefore, this study aims to determine the temperature and the emissions resulting from co-firing of palm kernel shell biomass. This research was conducted using the Computational Fluid Dynamics method on stoker boiler model. The test parameters of the research was the maximum and average temperatures in the furnace, and the average and maximum fractions of CO2, SO2 in the stoker boiler furnace. From the research conducted, it was found that the resulting combustion temperature decreased as the co-firing fraction of the biomass increased. However, CO2 gas emissions increased and SO2 decreased with increasing fraction of co-firing biomass which showed a decrease in harmful gas emissions and complete combustion that occurred in the furnace.

On the uniqueness of 3-D inhomogeneous viscous incompressible magnetohydrodynamic equations with bounded density

In this paper, we prove the uniqueness of the global solutions constructed by Xu et al. (2022).

A novel method for estimation of electrode kinetics parameters

PurposeThe purpose of this paper is to extract electrochemical reaction kinetics parameters, such as Tafel slope, exchange current density and equilibrium potential, which cannot be directly measured, this study aims to propose an improved particle swarm optimization (PSO) algorithm.Design/methodology/approachIn traditional PSO algorithms, each particle’s historical optimal solution is compared with the global optimal solution in each iteration step, and the optimal solution is replaced with a certain probability to achieve the goal of jumping out of the local optimum. However, this will to some extent undermine the (true) optimal solution. In view of this, this study has improved the traditional algorithm: at each iteration of each particle, the historical optimal solution is not compared with the global optimal solution. Instead, after all particles have iterated, the optimal solution is selected and compared with the global optimal solution and then the optimal solution is replaced with a certain probability. This to some extent protects the global optimal solution.FindingsThe polarization curve plotted by this equation is in good agreement with the experimental values, which demonstrates the reliability of this algorithm and provides a new method for measuring electrochemical parameters.Originality/valueThis study has improved the traditional method, which has high accuracy and can provide great support for corrosion research.

Calculation on maximum output power of wave energy-PTO system

The energy conversion efficiency of wave energy device is one of the key problems in the large-scale utilization of wave energy. The study of the maximum output POWER of PTO (Power-Take-Off) system provides a theoretical reference for the efficient utilization of energy. In this paper, genetic algorithm and adaptive differential evolution algorithm based on neighborhood search are used to optimize the two-objective multi-order differential equations for the maximum output power of PTO system, and the global optimal solution is obtained. Compared with the traversal algorithm, the algorithm involved in this paper is efficient and the optimal output power obtained can provide a scientific basis for the structural optimization design and material selection of the wave energy device.