Global Optimization Algorithm Research Articles

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

AbstractGeomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

Theta-mechanism based cluster search algorithm for global constrained optimization

This study concerns constructing an evolutionary search system to solve the global constrained optimization problems. Firstly, we proposed a hybrid constraint-handling method, called theta-mechanism, which blends two types of constraint-handling functions and alternates use of them in the searching process to balance two competing objectives: seeking as much as possible feasible regions and quickly converging to the optimum point in the found feasible regions. Secondly, to enable the search system to cooperate well with the theta-mechanism, we designed the cluster search algorithm (CSA) and developed the search reachability analysis (SRA) method. Based on SRA, we evaluated the characteristics of several typical search operators in order to assemble them into different operator combinations in CSA to maximize its performance, which enables CSA with theta-mechanism to accomplish the two inconsistent search objectives effectively. We tested the proposed method on 18 benchmark functions from IEEE CEC2010 and 32 real-world constrained optimization problems collected in IEEE CEC2020. Our results show the CSA with theta-mechanism is more competitive than the existing state-of-the-art approaches.

Structural Determination and Hierarchical Evolution of Transition Metal Clusters Based on an Improved Self-Adaptive Differential Evolution with Neighborhood Search Algorithm.

Determining the optimal structures and clarifying the corresponding hierarchical evolution of transition metal clusters are of fundamental importance for their applications. The global optimization of clusters containing a large number of atoms, however, is a vastly challenging task encountered in many fields of physics and chemistry. In this work, a high-efficiency self-adaptive differential evolution with neighborhood search (SaNSDE) algorithm, which introduced an optimized cross-operation and an improved Basin Hopping module, was employed to search the lowest-energy structures of CoN, PtN, and FeN (N = 3-200) clusters. The performance of the SaNSDE algorithm was first evaluated by comparing our results with the parallel results collected in the Cambridge Cluster Database (CCD). Subsequently, different analytical methods were introduced to investigate the structural and energetic properties of these clusters systematically, and special attention was paid to elucidating the structural evolution with cluster size by exploring their overall shape, atomic arrangement, structural similarity, and growth pattern. By comparison with those results listed in the CCD, 13 lower-energy structures of FeN clusters were discovered. Moreover, our results reveal that the clusters of three metals had different magic numbers with superior stable structures, most of which possessed high symmetry. The structural evolution of Co, Pt, and Fe clusters could be, respectively, considered as predominantly closed-shell icosahedral, Marks decahedral, and disordered icosahedral-ring growth. Further, the formation of shell structures was discovered, and the clusters with hcp-, fcc-, and bcc-like configurations were ascertained. Nevertheless, the growth of the clusters was not simply atom-to-atom piling up on a given cluster despite gradual saturation of the coordination number toward its bulk limit. Our work identifies the general growth trends for such a wide region of cluster sizes, which would be unbearably expensive in first-principles calculations, and advances the development of global optimization algorithms for the structural prediction of clusters.

Recent advances in the interface structure prediction for heteromaterial systems

The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.

Using a novel approach to characterize the surface reactivities of silica-rich ferrihydrite and biogenic cyanobacteria-ferrihydrite aggregates and the implications for Archean ocean geochemistry

Precambrian banded iron formations (BIF) are iron- and silica-rich chemical sedimentary rocks that are commonly used as paleo-redox proxies for Archean and Paleoproterozoic seawater geochemistry. At the onset of the Great Oxidation Event (herein GOE) around 2.4 Ga, cyanobacteria flourished with increasing nutrient fluxes due to oxidative weathering on land. In turn, this led to increased primary productivity that facilitated the permanent shift from a reducing Earth atmosphere to an oxidizing one. Interestingly, the duration of GOE also overlapped with one of the most prolific periods of BIF deposition.It is widely accepted that cyanobacteria were likely responsible for BIF formation during the GOE. Oxidation of dissolved Fe(II) by oxygen produced from cyanobacteria forms a metastable and amorphous mineral phase ferrihydrite, Fe(OH)3. As an essential component in both ancient BIF deposits and various modern ecosystems, the surface reactivity of ferrihydrite has been extensively studied under different conditions (i.e., pH and ionic strengths). Not only are the highly reactive surfaces of ferrihydrite particles important shuttles for trace element transport from the water column to the sediment pile, but previous studies have also demonstrated that cyanobacterial cells and ferrihydrite tend to aggregate at seawater pH. This means that ferrihydrite was also a vector for the transport of organic carbon to the seafloor. However, a complicating issue is how co-ions affect the surface reactivity of ferrihydrite, specifically dissolved silica which was abundant in ancient seawater. Although previous studies have demonstrated that silica can passivate the surface reactivity of ferrihydrite, what remains unclear is how silica impacts ferrihydrite-biomass aggregation. To fill this knowledge gap, we formed both silica-spiked ferrihydrite and cyanobacteria-ferrihydrite aggregates in situ and subsequently conducted empirical potentiometric acid-base titrations and Cd adsorption experiments on the fresh aggregate samples at three different ionic strengths (0.56 M, 0.1 M and 0.01 M). We minimized sample processing (i.e., drying and powdering) to a simple washing step, in which the aggregate pellets remained hydrated to avoid any mineral transformation thus altering their true surface reactivity in seawater. Experimental results were then fitted with non-electrostatic model to predict both surface charges and metal-adsorption behavior of ferrihydrite aggregates. Different from previous surface-complexation modelling studies, here we used a novel and more powerful modelling program called Phreefit. It utilizes the global optimization algorithms instead of more commonly used Newton-Raphson method in FITEQL program, which is often too limited for precisely modelling complex systems such as the two samples in this study. Furthermore, we also measured the surface charges of both samples over the pH range from 3 to 9 on a Malvern Zetasizer and characterized the surface functional groups through Fourier-Transform Infrared Spectroscopy to help with our interpretation of the experimental data.Preliminary results show that cyanobacteria-ferrihydrite aggregates formed primarily due to ionic bridging. Cyanobacterial cells likely facilitated the precipitation of dissolved silica. Findings from titration and Cd adsorption experiments indicate that the surface reactivity and capacity of both silica-rich ferrihydrite cyanobacteria-ferrihydrite aggregates to adsorb trace elements differ from their individual components, likely due to site blockage. This distinction is particularly prominent when considering the expected Archean seawater pH from 6 to 8. This disparity implies that the biogenic ferrihydrite aggregates do not exhibit an additive surface reactivity, which is in agreement with similar previous studies. Our combined results are crucial to accurately predict the adsorption of trace elements onto the aggregate surface and, ultimately, comprehend the archive of trace elements in sedimentary rocks used to reconstruct Precambrian ocean chemistry.

Differential evolution with migration mechanism and information reutilization for global optimization

Differential evolution (DE) is an efficacious global optimization algorithm, and many variants have been advanced since its inception. During the iterative search process, any individual in any DE-type algorithm can likely locate a local optimum, and once that happens it may need many attempts for this individual to find another better solution, thus leading to ineffective consumption of computing resources and decline in the opportunity to search other promising regions. Therefor, this article proposes a DE with migration mechanism and information reutilization (MIDE). Specifically, a migration mechanism is first presented to make individuals located at local optima abandon current locations and move to other regions to continue their search, tending to solve the problem above. Meanwhile, a new mutation strategy named DE/pbest/1 with external archive is introduced to reutilize abandoned local optima to provide helpful information of evolution. Additionally, the settings of control parameters associated with this mutation strategy are designed in such a manner that they can contribute to well balancing exploration and exploitation. To evaluate the performance of MIDE, extensive experiments are conducted on CEC 2017 and CEC 2014 test suites, and the comparison results between MIDE and 19 competitors (including 13 state-of-the-art DE variants and six winner algorithms of CEC 2014 and CEC 2017 competitions) demonstrate MIDE's competitive performance.

Modified prairie dog optimization algorithm for global optimization and constrained engineering problems.

The prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources. When encountering natural enemies, they emit different sound signals to remind their companions of the dangers. According to this unique information exchange mode, we propose a randomized audio signal factor to simulate the specific sounds of prairie dogs when encountering different foods or natural enemies. This strategy restores the prairie dog habitat and improves the algorithm's merit-seeking ability. In the initial stage of the algorithm, chaotic tent mapping is also added to initialize the population of prairie dogs and increase population diversity, even use lens opposition-based learning strategy to enhance the algorithm's global exploration ability. To verify the optimization performance of the modified prairie dog optimization algorithm, we applied it to 23 benchmark test functions, IEEE CEC2014 test functions, and six engineering design problems for testing. The experimental results illustrated that the modified prairie dog optimization algorithm has good optimization performance.

A differential evolution with autonomous strategy selection and its application in remote sensing image denoising

Differential evolution (DE) is an efficient and effective global optimization algorithm, and many DE variants with adaptive strategy selection have been proposed. However, among the existing DE variants with adaptive strategy selection, only a few last generations of population information are utilized to conduct statistical analysis for strategy selection, and the population cumulative historical experience information does not be fully used to assist population to effectively search. In this paper, a DE with autonomous strategy selection (ASS-DE) is proposed to make full use of the population cumulative historical experience information. In ASS-DE, an individual can autonomously choose the better mutation strategy guided by the cumulative historical experience. As the same time, a parameter updating mechanism with archive is introduced to assign appropriate control parameters to the strategy. Additionally, an evolutionary learning mechanism based on individual similarity is proposed to guide the learning of historical experience information. To verify the performance of ASS-DE, CEC2015 and CEC2017 benchmark functions and the real-world optimization problem of wavelet parameter optimization in remote sensing image denoising is compared with some state-of-the-art intelligence algorithms. The experimental results show that ASS-DE obtains the promising performance compared with other DE variants. The source code of ASS-DE can be found in GitHub at https://github.com/Ramessis/ASS-DE_denoising.git.

Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

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.