Global Optimization Algorithm Research Articles

A sequence decomposition genetic algorithm for petrophysical inversion with spatial correlation

Predicting petrophysical properties based on seismic attributes is essential for accurate subsurface characterization. Typically, a forward model is first established to relate the petrophysical properties to geophysical measurements, and then the petrophysical properties are inverted from the actual measurements. Imposing prior constraints of the spatial relationship is also desirable to regularize the ill-posed inverse problem. However, petrophysical inversion involving spatial correlation presents a high-dimensional optimization problem with multiple local extrema. This problem can be challenging to solve, particularly when discrete variables (such as lithofacies) and continuous variables (such as porosity and fluid saturation) are considered together. Therefore, we develop a global optimization algorithm that uses the Markov chain decomposition and a genetic algorithm for a joint discrete-continuous petrophysical inversion with spatial correlation. The innovation of this algorithm is to spatially decouple the sequence optimization problem into multiple low-dimensional subproblems and overcome the initial value dependence to converge to the global optimal solution. Furthermore, an approximation operation is introduced to expedite the optimization algorithm. Numerical experiments show that after algorithmic acceleration, the spatially correlated inversion requires the same computational time as a simple spatially uncorrelated inversion and achieves significantly better accuracy than the latter. Finally, we apply the method to a case study in East China, wherein the lithofacies, clay content, porosity, and water saturation are inferred from the data of the wave velocities and density. The inversion results around the wells generally match the well-logging data, verifying the feasibility of the new method.

A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator

This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.

A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization

The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.

A New Global Optimization Algorithm for Mixed-Integer Quadratically Constrained Quadratic Fractional Programming Problem

A New Global Optimization Algorithm for Mixed-Integer Quadratically Constrained Quadratic Fractional Programming Problem

An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction

A Hybrid Multi-population Optimization Algorithm for Global Optimization and Its Application on Stock Market Prediction

Improvement of augmented free-water flash algorithm based on HV mixing rule and simulated annealing optimization algorithm

The calculation of three-phase equilibrium in CO2-hydrocarbon-water mixtures holds significant importance within numerical simulations, particularly in applications such as CO2-enhanced oil recovery and carbon dioxide sequestration. However, due to the non-ideality of CO2 and the polarity of water, three-phase equilibrium calculations often encounter convergence challenges. The augmented free-water flash algorithm [6], specifically focusing on CO2 dissolution in the aqueous phase, addresses the convergence challenges encountered by traditional three-phase flash algorithms. Despite its accuracy diminishes under conditions of high pressure and high CO2 concentrations, limiting its capability to accurately describe CO2 dissolution in oil-gas-water multiphase systems. The GE (excess Gibbs free energy) type mixing rule can be effectively integrated with equations of state, including PR and SRK, by utilizing activity models like NRTL and UNIFAC, which can be applied to the calculation of thermodynamic parameters for both nonpolar and polar systems and high-pressure complex systems. In this study, based on the augmented free-water assumption, we have developed a new augmented free-water three-phase flash algorithm for CO2/hydrocarbon/water mixtures. This algorithm integrates the PR equation of state with the NRTL activity model, employing the HV mixing rule for CO2-water interactions and the Van der Waals rule for other non-polar components. Furthermore, to enhance computational efficiency, the algorithm incorporates the simulated annealing global optimization algorithm, replacing Newton iteration to more effectively identify the global minima of the complex objective function. The example calculations show that the improved augmented free-water flash algorithm has higher accuracy and efficiency than the traditional augmented free-water flash algorithm. The augmented free-water three-phase flash algorithm proposed in this paper offers a precise model for phase equilibrium calculations essential for numerical simulations in CO2-enhanced oil recovery and carbon dioxide sequestration.

An efficient global optimization algorithm for a class of linear multiplicative problems based on convex relaxation

An efficient global optimization algorithm for a class of linear multiplicative problems based on convex relaxation

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’.

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.

ICSOMPA: A novel improved hybrid algorithm for global optimisation

ICSOMPA: A novel improved hybrid algorithm for global optimisation

Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters.

In the realm of structural and bonding investigations within chemical systems, elucidating global minimum energy configurations stands as a paramount goal. As the systems increase in size and complexity, this pursuit becomes progressively challenging. Herein, we introduce Bonobo optimizer (BO), a metaheuristic algorithm inspired by the social and reproductive behaviors of bonobos, to the domain of chemical problem solving. Focusing on small carbon clusters, this study systematically evaluates BO's performance, showcasing its robustness and efficiency. Parametric studies highlight the algorithm's adaptability, consistently converging to global minimum structures. Rigorous statistical validation supports the results, and a comparative analysis against established global optimization algorithms underlines BO's superior efficiency. This exploration extends the applicability of BO to the optimization of atomic clusters, providing a promising avenue for future advancements in computational chemistry.

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

Integrated design of insertions-extractions performance and contact reliability of spring-wire socket electrical connector

With the improvement of the reliability and lifespan of electrical connectors, the traditional single objective design focusing solely on performance or reliability has become inadequate to meet the requirements. This paper analyzes the failure mechanism of a specific electrical connector under the conditions of insertions-extractions. It establishes a contact degradation model and an integrated design model that considers mechanical properties and reliability. By analyzing the integrated design model, the optimization variables have been defined as the radius of the spring wire, the radius of the pin, the inner radius of the inner sleeve, the length of the inner sleeve, and the inclination of the spring wire. Additionally, the objective function of optimization has been determined. The optimal solutions that satisfy the constraints were calculated using the global optimization algorithm. The optimization results not only achieve the goal of high reliability but also the goal of extended mechanical life.

A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering applications. As a result, this study attempts to boost the efficiency of the recently introduced bio-inspired algorithm, the Tunicate Swarm Algorithm (TSA), which is motivated by the foraging and swarming behaviour of bioluminescent tunicates residing in the deep sea. Like other algorithms, the TSA has certain limitations, including getting trapped in the local optimal values and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. To overcome these shortcomings, a novel multi-strategy ameliorated TSA, termed the Quasi-Oppositional Chaotic TSA (QOCTSA), has been proposed as an enhanced variant of TSA. This enhanced method contributes the simultaneous incorporation of the Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) mechanisms to effectively balance exploration and exploitation. The implementation of QOBL improves convergence accuracy and exploration rate, while the inclusion of a CLS strategy with ten chaotic maps improves exploitation by enhancing local search ability around the most prospective regions. Thus, the QOCTSA significantly enhances convergence accuracy while maintaining TSA diversification. The experimentations are conducted on a set of thirty-three diverse functions: CEC2005 and CEC2019 test functions, as well as several real-world engineering problems. The statistical and graphical outcomes indicate that QOCTSA is superior to TSA and exhibits a faster rate of convergence. Furthermore, the statistical tests, specifically the Wilcoxon rank-sum test and t-test, reveal that the QOCTSA method outperforms the other competing algorithms in the domain of real-world engineering design problems.

Minimizing occupant loads in vehicle crashes through reinforcement learning-based restraint system design: assessing performance and transferability

AbstractThe optimization of mechanical behavior in safety systems during crash scenarios consistently poses challenges in vehicle development. Hence, a reinforcement learning-based approach for optimizing restraint systems in frontal impacts is proposed. The trained agent, which adjusts five parameters simultaneously, is capable of minimizing loads on a seen and unseen anthropomorphic test device on the co-driver position and is thus able of transferring knowledge. A hundred times higher rate of convergence to reach a similar optimum compared to a global optimization algorithm has been achieved.

A novel differential evolution algorithm with multi-population and elites regeneration.

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm.

Evolutionary Computation in bioinformatics: A survey

Bioinformatics is a subject that studies life phenomena by using mathematical and information science theories and techniques. Its main tasks, such as DNA sequence comparison, protein structure prediction and cell metabolism analysis, can be regarded as complex optimization problems with different characteristics. Evolutionary computing is a kind of global optimization algorithm inspired by nature. Over the years, scholars have accumulated fruitful results in solving complex optimization problems such as large-scale, dynamic, multi-modal, multi-objective and multi-constrained problems by using Evolutionary Computation algorithms, and have successfully applied to the above optimization tasks in bioinformatics. This paper mainly summarizes the work of Evolutionary Computation technologies in bioinformatics from 2019 to 2023 at multiple levels, including Genomics, Proteomics, metabolomics and molecular networks related optimization tasks, as well as further applications in disease diagnosis and drug development.

Designing desalination MXene membranes by machine learning and global optimization algorithm

The development of energy-efficient and low-cost desalination techniques is crucial. Among these techniques, reverse osmosis (RO) is considered one of the most promising solutions for addressing the global water crisis. It has been widely implemented for both large-scale and distributed water desalination. One such promising desalination membrane is MXene with nanopores, thanks to its unique properties. However, accurately predicting the performance of the desalination process or designing new materials is challenging due to the complexity of the process and the various tunable properties of the membranes and nanopores themselves. The combination of machine learning (ML) and global optimization algorithms offers a superior approach to material design from multiple perspectives. Our study demonstrates that Particle Swarm Optimization (PSO) exhibits faster convergence speed and stability compared to Genetic Algorithms. Ti3C2O2 with a specific charge on the nanopore mouth is an excellent candidate, as determined by the particle swarm optimization algorithm. By analyzing water density and ion density along the nanopore, we gain a deep understanding of how pore charge and functionalized groups affect salt rejection and water permeation. The integration of ML and global optimization algorithms can facilitate the design of materials with outstanding desalination performance.

Two new members of the covalent organic frameworks family: Crystalline 2D-oxocarbon and 3D-borocarbon structures

Oxocarbons, known for over two centuries, have recently revealed a long-awaited facet: two-dimensional crystalline structures. Employing an intelligent global optimization algorithm (IGOA) alongside density-functional calculations, we unearthed a quasi-flat oxocarbon (C6O6), featuring an oxygen-decorated hole, and a novel 3D-borocarbon. Comparative analyses with recently synthesized isostructures, such as 2D-porous carbon nitride (C6N6) and 2D-porous boroxine (B6O6), highlight the unique attributes of these compounds. All structures share a common stoichiometry of X6Y6 (which we call COF-66), where X = B, C, and Y = B, N, O (with X ≠ Y), exhibiting a 2D-crystalline structure, except for borocarbon C6B6, which forms a 3D crystal. In our comprehensive study, we conducted a detailed exploration of the electronic structure of X6Y6 compounds, scrutinizing their thermodynamic properties and systematically evaluating phonon stability criteria. With expansive surface areas, diverse pore sizes, biocompatibility, π-conjugation, and distinctive photoelectric properties, these structures, belonging to the covalent organic framework (COF) family, present enticing prospects for fundamental research and hold potential for biosensing applications.