Global Optimization Algorithm Research Articles

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.

Enhancing population diversity based gaining-sharing knowledge based algorithm for global optimization and engineering design problems

Gaining-sharing knowledge based algorithm (GSK) is a recently emerged meta-heuristic algorithm based on human behavior and has been successfully applied to solve various optimization problems. However, GSK tends to get trapped in local optimum due to the rapid loss of population diversity during the optimization process, resulting in an imbalance between exploration and exploitation. To overcome this deficiency, this paper proposes an enhancing population diversity based GSK (EPD-GSK) framework. The proposed EPD-GSK framework incorporates three components: (1) The utilization of Sobol sequence with low divergence to initialize the population, enhancing the diversity of initial solutions. (2) The integration of the Cauchy mutation strategy in the junior phase to perturb individuals and expand the search space. (3) The application of the reverse learning update mechanism in the senior phase, increasing the likelihood of escaping local optimum. These techniques promote population diversity throughout the exploration and exploitation stages. The proposed EPD-GSK framework was evaluated on CEC2017, CEC2020, and the latest CEC2022 test suites as well as on four constrained real-world engineering design problems. The experimental results demonstrate that EPD-GSK can effectively improve the performance of various existing GSK algorithms. Furthermore, EPD-GSK also exhibits better performance compared with other state-of-the-art algorithms.

MECSBO: Multi‐strategy enhanced circulatory system based optimisation algorithm for global optimisation and reliability‐based design optimisation problems

MECSBO: Multi‐strategy enhanced circulatory system based optimisation algorithm for global optimisation and reliability‐based design optimisation problems

Improved optimal foraging algorithm for global optimization

Improved optimal foraging algorithm for global optimization

Reinforcement learning marine predators algorithm for global optimization

Reinforcement learning marine predators algorithm for global optimization