Global Optimization Algorithm Research Articles

Conceptual Design of Solid-State Li-Battery for Urban Air Mobility

The negative impact of internal combustion engines on the environment is a major concern in metropolitan areas due to the continued rapid growth and high overall level in the number of vehicles, population, and traffic congestion. Electric vertical take-off and landing (eVTOL) aircraft promises a new era for urban regional transportation and air mobility to address the challenges mentioned above. Nonetheless, providing electrical energy storage systems, like batteries, is one of the key issues with such aircraft. Here, the non-flammable technology of all-solid-state Li batteries with high theoretical gravimetric energy is an attractive option. Modelling allows for a knowledge-driven assessment of the potential of this technology. We here used a combination of a pseudo-2-dimensional cell model with a microstructure surrogate model approach to acquire a better understanding of the effect of the cathode microstructure on the internal process limitations. This model is incorporated into a global optimisation algorithm to predict optimum battery size with respect to the dynamic load demand of eVTOL. When carbon black and active materials are premixed, the battery performs better than when solid electrolyte and active materials are premixed, particularly for low amounts of carbon black in the cathode combination, i.e., 5%. Further, results indicate that future electrification of transportation powertrains would necessitate optimising the composition and distribution of electrode components to fulfil the high demands for power and energy density. By enhancing transport through the microstructure and improving the material’s intrinsic conductivity, it is possible to significantly increase the effective diffusivity and conductivity of ASSB, and hence the mission range.

Shape optimization of superconducting transmon qubits for low surface dielectric loss

Surface dielectric loss from superconducting transmon qubits is believed to be one of the dominant sources of decoherence. Reducing surface dielectric loss is crucial for achieving a high quality factor and a long relaxation time (T 1), which can be engineered by carefully changing the geometry of a transmon. In this paper, we present a shape-optimization approach to reduce surface dielectric loss in a transmon qubit. The capacitor pads and junction wires of transmons are shaped as spline curves and optimized through the finite-element method and a global optimization algorithm. We find that the participation ratio of capacitor pads and junction wires can be reduced by % and % compared to previous designs, while the overall footprint and anharmonicity are maintained at acceptable values. As a result, the two-level system-limited quality factor and the corresponding T 1 were increased by 21.6%.

Analysis of Preliminary Impulsive Trajectory Design for Near-Earth Asteroid Missions under Approaching Phase Constraints

This study investigates the preliminary trajectory design for high-thrust missions to near-Earth asteroids (NEAs), considering distance and phase angle constraints during the approaching phase to enable pre-rendezvous optical navigation and the scientific identification of asteroids. A global optimization algorithm called monotonic basin hopping is used to design -optimal impulsive trajectories both with and without constraints. Comparisons reveal that extending the final leg of the unconstrained reference trajectory and incorporating a few deep-space maneuvers in that final leg can yield a constrained trajectory with a increase of only a few percent. The effects of the phase angle and minimum distance constraint on are also examined. The results indicate that in -optimal constrained trajectories, an additional deep-space maneuver enables the redistribution of maneuvers in the last leg to ideally insert the spacecraft into the constraint cone. However, additional small maneuvers may be necessary at times to ensure that the spacecraft remains within the cone. Based on these findings, we present a two-step approach for the preliminary design of constrained trajectories for NEA missions based on global optimization algorithms. This approach serves as a valuable tool for initial mission design and trade-off analyses involving constraints, fuel usage, and transfer durations.

A study on light extinction model and inversion of mixed particle system based on Monte Carlo method

The extinction characteristics of mixed particles, which are composed of various types of particles, appear to be more complex. To statistically analyze photon destinations and investigate the extinction characteristics of mixed particles by tracing all underlying events in detail, a Monte Carlo method-based extinction model has been developed. Meanwhile, an extinction spectrum measurement system was designed. A series of experiments for both single-type particle and mixed-particle systems were performed. Afterwards, global optimization algorithms are introduced to perform simultaneous inversion of sizes of mixed particles and mixing ratio based on the experimental spectrum. The inversion results show that the error for any single parameter is <2%. Performing a simultaneous inversion for particle sizes and mixing ratios leads to an increase in the relative error, but it does not exceed 9%. It indicates that the model could simultaneously invert the mixing ratio and handle particle systems with two different sizes.

Global optimization and structural analysis of Coulomb and logarithmic potentials on the unit sphere using a population-based heuristic approach

Global optimization of high-dimensional non-convex functions arises in many important applications and researches. In this paper, we focus on the minimum energy configuration problem on the unit sphere, whose goal is to determine the minimum-energy configuration of N particles interacting via the standard Coulomb potential or the discrete logarithmic potential on the unit sphere. This is a classic global optimization problem with many important applications in mathematics, physics, biology and chemistry, and is one of the 18 great mathematical problems for the twenty-first century listed by the Fields Medal winner (Smale, 1998). To determine the minimum-energy configuration for these two potential functions, we propose a population-based global optimization algorithm that combines a two-phase local optimization method with early-stopping strategy, a random perturbation operator, a distance-based population update strategy using a novel distance function to measure the difference between solutions, and a population rebuilding method. Computational results show that the proposed algorithm is very competitive compared to the best known results in the literature. In particular, it improves the best-known result for one instance of the classic Thomson problem (i.e., N=360) widely studied in the literature. The configurations of instances with up to N=500 particles are systematically optimized for the first time by the proposed algorithm for the discrete logarithmic potential. The comparative study shows that the putative optimal configurations have a high similarity for the Coulomb and logarithmic potentials and that the defects play an important role in reducing the energy of configuration. The energies of particles on the putative optimal configurations are analyzed for the studied logarithmic potential.

A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems.

A totally coupled multi time-scale framework containing full parameters online identification and SOC real-time estimation of lithium-ion battery based on a fractional order model

Fractional order model (FOM) is one of the most promising choices for lithium-ion battery simulation and internal states estimation due to its high modeling accuracy. However, online identifying the order and parameters of FOM is extremely tough, which also makes it difficult to be applied in complex and variable operating conditions. To address this issue, this paper proposes a totally coupled multi time-scale framework that contains full parameters online identification of FOM and state-of-charge (SOC) real-time estimation of lithium-ion battery. Firstly, a novel global optimization algorithm named improved particle swarm optimization is designed to update the order of FOM on macro-time scale with low computational burden. On the basis of the determined order, the rest parameters of FOM are further identified by forgetting factor recursive least square algorithm on micro-time scale. With those online identified parameters, adaptive extended Kalman filter is finally adopted to track battery SOC in every instant. The estimation accuracy, adaptability to different operating temperatures and battery aging, and robustness ability against uncertain initialization of the developed method is verified under Federal Urban Driving Schedule tests. The corresponding results demonstrate that the proposed method can realize accurate full parameters online identification of FOM and precise SOC real-time estimation against different temperatures, battery aging states and initialization. Using the developed method, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation of the fresh battery under temperature ranging from 0 °C to 50 °C can be limited below 0.9 % and 1.1 %, respectively. Even if the SOC-OCV relationship is not timely updated, those two indexes of the aged battery achieved by the proposed method can still be controlled within 3 % under temperature ranging from 0 °C to 50 °C. Moreover, the comparison with other typical FOM-based estimation methods is also conducted, whose results indicate that the developed method has apparently superior comprehensive performance on estimation accuracy and adaptation.

Globaliosios sijyno optimizacijos uždaviniai

Important problem in civil engineering is obtaining optimal pile placement schemes for grillage-type foundations in order to reduce the number of piles and reactive forces arising in piles. The idealizations of problem and formulation for local and global optimization are discussed, highlighting the must for global optimization. The grillage is discretized using finite element method. Sensitivity of objective function is obtained analytically. The solution technology for a class of aforementioned problems is proposed based on the use of global optimization algorithms of package GAMS, which are implemented as ``black boxes”. Results of computational experiments are presented.

A Quantum Particle Swarm Optimization Algorithm Based on Aggregation Perturbation

A quantum particle swarm hybrid optimization algorithm based on aggregation disturbance is proposed for inventory cost control. This algorithm integrates the K-means algorithm on the basis of traditional particle swarm optimization, recalculates the clustering center, initializes stagnant particles, and solves the problem of particle aggregation. Introducing chaos mechanism into the algorithm, changing the position of particles, enhancing their activity, and improving the algorithm's global optimization ability. At the same time, define the aggregation disturbance factor, determine the current state of particles, optimize speed and position to accelerate escape, and solve the problem of particles falling into local optima. Experiments show that M-IKPSO algorithm has strong stability, fast Rate of convergence and high accuracy compared with other algorithms, and the improvement effect is significant.

Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm

The wake in a large-scale Floating Offshore Wind Farm (FOWF) can reduce the wind speed in downstream areas, thereby affecting and reducing the power production of FOWF. To maximize the power production of the FOWF, it is necessary to solve the power optimization problem and obtain the optimal control actions for the coordinated operation of wind turbines. Due to the complexity of the optimization problem, there remains difficulty in applying swarm intelligence algorithms, and thus we propose a dimensionality reduction-based surrogate-assisted framework. Firstly, a low-dimensional surrogate model is constructed using data generated by swarm intelligence algorithms and dimensionality reduction algorithms during the optimization process. Secondly, based on the low-dimensional surrogate model, multi-subswarms pre-screening is carried out to filter out poor solutions in the population and reduce the time consumption of calculating the objective function. Thirdly, the trust region range is determined and a local surrogate model is constructed for multiple local searches using the results of the multi-subswarms pre-screening, and the elite individuals obtained further guide the population individuals to search for optimization, improving the optimization efficiency of the algorithm. Finally, the proposed method is simulated and verified in a FOWF with 64 wind turbines. Under the wind direction where the wake effect is severe, the steady-state power production can be increased by more than 6%. Compared with conventional swarm intelligence algorithms and numerical solution methods, the proposed method produces more power while having smaller time cost under different wind conditions.

Gas permeation through carbon membranes: Model development and experimental validation

With a growing interest in carbon membranes for gas separation, understanding their performance and behaviour is essential for proper design of the membrane separation. Currently not many models exist that correctly describe transport through carbon membranes due to its complex nature. This work attempts to implement a general modelling approach which describes several key transport phenomena inside carbon membranes. The approach assumes a membrane wall to be a bundle of pores with parallel transport mechanisms using the pore size distribution as a weight factor to sum the different transport phenomena. This work adapts this approach specifically for carbon membranes, additionally accounting for molecular sieving and pore blocking effects. Imposing realistic boundary conditions, the model is solved using global optimization algorithms. For testing, four different CMSMs have been produced with hydroquinone and novolac precursors. Pure- and mixed gas permeation tests are done for these CMSMs with H2, N2, and CO2 and the model is fit to this permeation data. Fitting results with pure gas measurements show the model is able to predict the contributions of different mass transport mechanism for the different membranes. This is validated by comparing these results to gas-pair permselectivity data. The model is furthermore fit to mixed gas data. Existence of multi-component effects shows that the model could be further improved. Overall, the model presented in this work is shown to be able to describe complex mass transport behaviour for various different carbon membranes.

SLDChOA: a comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design

SLDChOA: a comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design

Nizar optimization algorithm: a novel metaheuristic algorithm for global optimization and engineering applications

Nizar optimization algorithm: a novel metaheuristic algorithm for global optimization and engineering applications

Simulated Annealing Search Technique

Simulated Annealing is a stochastic global search optimization algorithm. The algorithm is inspired by annealing in metallurgy where metal is heated to a high temperature quickly, then cooled slowly, which increases its strength and makes it easier to work with. algorithm comes from annealing in metallurgy, a technique involving heating and controlled cooling of a material to alter its physical properties. Keywords: optimization, bound-constrained, local minima, travelling salesman problem.

A new modified bat algorithm for global optimization

Bat Algorithm, is an evolutionary computation technique based on the echolocation behaviour of microbats while looking for their prey. It is used to perform global optimization. It was developed by Xin-She Yang in 2010. Since then, it has extensively been applied in various optimization problems because of its simple structure and robust performance. Continuous, discrete, or binary, many variants were proposed over the last few years, with applications to solve real-world cases in different fields. Yet, it has one major drawback: its premature convergence due to a lack in its exploration ability. In this paper, we introduce a selection-based improvement and three other modifications to the standard version of this metaheuristic in order to enhance the diversification and intensification capabilities of the algorithm. The newly proposed method has been then tested on 20 standard benchmark functions and the CEC2005 benchmark suit. Some non-parametric statistical tests were also used to compare the New Bat algorithm with other algorithms, and results indicate that the new method is very competitive and outperforms some of the state-of-the-art algorithms.

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach.

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection.

Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)'17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO.

Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems.

Electromagnetic induction (EMI) systems are used for mapping the soil's electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm-1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm-1, which is considerably lower than the RMSE values of up to 4.5 mSm-1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects.

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

AbstractA novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.

A competitive and collaborative-based multilevel hierarchical artificial electric field algorithm for global optimization

Competitive and collaborative strategies and topologies are among the most essential concepts and greatly influence the optimization ability of population-based optimization algorithms. To update individuals' information, this article proposes a multilevel hierarchical artificial electric field algorithm with competitive and collaborative strategies (PAEFA). The proposed algorithm constructs a multilevel structure and places them in specific layers. The whole population is divided into two groups of winners and losers by pairwise comparison of their fitness in the same layer. Losers collaborate with their respective winners, whereas winners collaborate with individuals who are on the upper layers. In the proposed algorithm, each individual has their own learning mechanism, which can learn from more than one exemplar, rather than only from the global best. With the knowledge of this structure, the diversity of the population increases, which strengthens the performance of the scheme. To verify the adaptability of the proposed algorithm, extensive experiments are performed on the CEC 2017 test suite at 30, 50, and 100 dimensions. We have studied the diversity factor of PAEFA using all three dimensions. These experiments suggest that PAEFA outperforms over thirty state-of-the-art algorithms in terms of accuracy, statistical results, and convergence speed while achieving comparable computational time in most cases and showing the validity of results. The PAEFA algorithm achieves superior performance compared to other state-of-the-art algorithms on 87.60% and 80.05% of problems in terms of accuracy and statistical significance across all three dimensions, respectively.