Efficient Optimization Approach Research Articles

Expected coordinate improvement for high-dimensional Bayesian optimization

Bayesian optimization (BO) algorithm is very popular for solving low-dimensional expensive optimization problems. Extending Bayesian optimization to high dimension is a meaningful but challenging task. One of the major challenges is that it is difficult to find good infill solutions as the acquisition functions are also high-dimensional. In this work, we propose the expected coordinate improvement (ECI) criterion for high-dimensional Bayesian optimization. The proposed ECI criterion measures the potential improvement we can get by moving the current best solution along one coordinate. The proposed approach selects the coordinate with the highest ECI value to refine in each iteration and covers all the coordinates gradually by iterating over the coordinates. The greatest advantage of the proposed ECI-BO (expected coordinate improvement based Bayesian optimization) algorithm over the standard BO algorithm is that the infill selection problem of the proposed algorithm is always a one-dimensional problem thus can be easily solved. Numerical experiments show that the proposed algorithm can achieve significantly better results than the standard BO algorithm and competitive results when compared with five high-dimensional BOs and six surrogate-assisted evolutionary algorithms. This work provides a simple but efficient approach for high-dimensional Bayesian optimization. A Matlab implementation of our ECI-BO is available at https://github.com/zhandawei/Expected_Coordinate_Improvement.

An advanced high dimensional model representation approach for internal combustion engine modeling and optimization

This work introduces an efficient data-driven approach for engine performance modeling and optimization. A highly accurate and robust high-dimensional model representation (HDMR) surrogate model is developed based on the dataset generated from a coupled KIVA4-CHEMKIN Ⅱ software. The HDMR model is integrated with a multi-objective optimization algorithm, enabling the automatic identification of optimal combustion strategies. Results suggest that the constructed HDMR model is able to accurately predict the engine performance and exhaust gas emissions with negligible CPU costs. Moreover, the model effectively identifies optimal engine operating conditions, leading to enhanced fuel efficiency.

Discrete level set method enhanced by conformal mapping: an efficient approach for topology optimization of piezoelectric energy harvesters

Discrete level set method enhanced by conformal mapping: an efficient approach for topology optimization of piezoelectric energy harvesters

An efficient optimization approach with mobility management for enhanced QoS and secure communication in flying adhoc networks

In biomedical Applications, Network plays a vital role to transmit the health-related information from patient to doctors which helps to save the human lives. During the data transmission, there is the possibility of data loss, delayed delivery, malicious attack and that leads to huge loss in human lives. To overcome this issue, a new technique Smart & secure Flying Adhoc Networks (FANET) architecture was proposed for the Quality-of-Service (QoS) improvement in biomedical applications. This research work helps society to save human lives during any emergency. With the integration of optimization and clustering techniques, the design configuration of the proposed architecture includes mobility management and secure transmission. The first phase includes sensing the patient's information by using Arduino IDE hardware. The sensed data will be transmitted through different types of networks including mobile ad-hoc networks, vehicular ad-hoc networks and flying ad-hoc networks. The routing protocol concentrates on clustering formation, mobility management, secure transmission and optimization. When compared to other type of networks, the proposed Smart & secure Flying Adhoc Networks (FANET) improves the network performance. The quality-of-service was achieved using the parameters such as network throughput, latency, delivery rate, routing overhead and control overhead.

Failure Probability-Based Optimal Seismic Design of Reinforced Concrete Structures Using Genetic Algorithms

Artificial intelligence (AI) has enabled several optimization techniques for structural design, including machine learning, evolutionary algorithms, as in the case of genetic algorithms, reinforced learning, deep learning, etc. Although the use of AI for weight optimization in steel and concrete buildings has been extensively studied in recent decades, multi-objective optimization for reinforced concrete (RC) and steel buildings remains challenging due to the difficulty in establishing independent objective functions and obtaining Pareto fronts. The well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient genetic algorithm approach for multi-objective optimization. In this work, the NSGA-II approach is considered for the multi-objective structural optimization of three-dimensional RC buildings subjected to earthquakes. For the objective of this study, two function objectives are considered: minimizing total cost and the probability of structural failure, which are obtained via several nonlinear seismic analyses of the RC buildings. Beams and columns’ cross-sectional dimensions are selected as design variables, and the Mexican Building Code (MBC) specifications are imposed as design constraints. Pareto fronts are obtained for two RC-framed buildings located in Mexico City (soft soil sites), which demonstrate the efficiency and accuracy of NSGA-II for structural optimization.

A simple and efficient approach for pore structure optimization and hydrophilic modification of PTFE nanofiber membrane

Polytetrafluoroethylene (PTFE) membranes have great potential for treating wastewater in harsh environment due to their excellent stability. However, difficulties in the pore structure optimization and functional modification of biaxial stretched PTFE membrane have limited its further applications in liquid separation. Herein, we presented a simple and efficient approach for the preparation and hydrophilic modification of electrospun PTFE nanofiber membranes. This method involved optimizing the pore structure of PTFE nanofiber membranes through adjusting the mass ratio of the spinning carrier polyethylene oxide (PEO) and PTFE, followed by introducing the acetalized polyvinyl alcohol (PVA) on the membrane’s pore surface to improve the hydrophilicity. The average pore size changed from 288.5 to 161.3 nm, while the water contact angle reduced from 135.7° to 50.9°, which not only increased the water permeate flux from 68.56 to 1056.16 L·m−2·h−1, but also achieved high rejection rates of 99.3 % and 97.3 % for carbon (1 μm) and SiO2 (100 nm) particles respectively. Meanwhile, the obtained PTFE nanofiber membrane also exhibited excellent hydrophilic stability after long term exposure to harsh environments (pH=2 HCl, pH=12 NaOH, 1000 ppm NaClO) and 100 friction cycles (5 g weights on 1000 mesh sandpaper). This approach of preparing hydrophilic PTFE nanofiber membranes showed significant potential for practical applications in wastewater treatment.

Highly efficient optimal decomposition approach and its mathematical analysis for solving fourth-order Lane–Emden–Fowler equations

In this paper, an optimal decomposition algorithm is introduced to solve a kind of nonlinear fourth-order Emden–Fowler equations (EFEs) that appear in many applied fields. Transforming the Emden–Fowler equation into a Volterra integral equivalent equation allows us to deal with the singularity at the endpoint x=0. This conversion also helps to reduce the computational cost of solving the problem. The existence and uniqueness of the solution of each integral equation obtained are established in the corresponding theorems. The convergence analysis further supports the theoretical findings. The accuracy and efficiency of the new method are tested against the existing method (Wazwaz et al., 2014) using numerous cases, and the results show that the presented scheme is a reliable method for computing approximate series solutions and even exact solutions. In addition, the new technique overcomes the drawback of the existing method, that provides only an approximation within a limited interval.

Adaptive topology optimization for enhancing resistance to brittle fracture using the phase field model

Computational cost is one of the challenges in the field of fracture resistance topology optimization. An efficient topology optimization approach is proposed for enhancing resistance to structural fracture based on the adaptive isogeometric–meshfree method. The mesh can be adaptively refined in the computational and design domains simultaneously to capture the fracture and structural boundary delicately, which significantly reduces the degree of freedom and improves the efficiency of the topology optimization problems related to crack propagation. The proposed approach naturally inherits the advantages of both the computer-aided design and continuity of the higher-order basis functions, where the geometry and the analysis models in the process of topology optimization are integrated. The problem is formulated to maximize the absorbed energy throughout the crack process using the solid isotropic material with penalization method under volume constraints. The phase field method is used for modeling crack propagation, thereby eliminating the need of an explicit representation of the crack surface and complex tracking procedures, while a delicate mesh is still required. With this approach, the external work required during the fracture process is maximized, and the crack growth is delayed remarkably. Several representative examples are demonstrated to verify the effectiveness of the present approach in fracture-resistance-enhanced topology optimization, and the computational cost shows remarkable improvement.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

An efficient data-driven approach for modeling and optimization of reactivity-controlled compression ignition engine fueled with polyoxymethylene dimethyl ethers and hydrogen

This paper presents an efficient data-driven approach for optimizing the utilization of PODEn and hydrogen in reactivity-controlled compression ignition (RCCI) engines. A novel high dimensional model representation (HDMR) technique is adopted to construct a highly accurate surrogate model for the RCCI engine based on the dataset generated by a detailed CFD model that has been calibrated against experimental data. The HDMR model is then coupled with Non-dominated Sorting Genetic Algorithm III (NSGA III), a multi-objective genetic algorithm, to search for the optimal operating conditions where the RCCI engine fueled by PODEn and H2 achieves the highest combustion performance. Results suggest that the constructed HDMR model is highly accurate, being able to predict the engine performance within milliseconds. The coupled HDMR-NSGA III approach successfully identifies the optimal engine operating condition, which significantly improves the combustion performance and reduces pollutant emissions compared with the base condition.

Path planning of humanoid robots on complex terrains using an efficient regression-based chaotic Harris Hawks optimization approach

PurposeOptimal navigation and trajectory planning are in high demand because of the rise in automated systems. This study aims to focus on implementing an intelligent regression-based chaotic Harris Hawk optimization (LR-CHHO) to achieve a globally optimal path free from collisions.Design/methodology/approachThis study removes the drawbacks of the existing HHO model in terms of its exploration and exploitation behaviors. After the threat is encountered, the improved controller is activated. The LR tool, here, avoids the issue related to the sensitivity of the model. The virtual Hawks, as per the HHO technique, are generated and trained to enhance the diversity in Hawks population. The final controller then calculates the optimal turn angle for the humanoid to avoid threats before reaching the goal.FindingsModel showed an overall improvement greater than 4% in the path and 9% in time compared with standard models in Terrains 1 and 2. Regarding energy efficiency, a significant improvement of more than 20% in the hip, 14% in the knee and 30% in the ankle was observed on both even and uneven terrains.Originality/valueThe originality of this study focuses on improving the diversity in the HHO population by introducing the LR-based model to help the humanoids find an optimal path to the goal. Although the basic model lacked an optimal solution because of sensitivity, less diversity, etc., the proposed model helped resolve the issue and achieve an optimal turning angle for the humanoids to trace the optimal path.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

Efficient simultaneous optimization approach for the synthesis of large-scale heat-integrated water networks

This study develops a generalized superstructure and a compact mixed-integer nonlinear programming (MINLP) model for achieving efficient energy and water utilization in designing integrated process water systems with large sizes. Utilizing non-isothermal mixing reduces the need for many heat exchangers for indirect heat recovery. Hence, an exchanger-centric modeling strategy for heat integration is embraced over a hot–cold streams matching-based approach. A novel two-step approach is proposed to address the nonconvex and nonlinear challenges and ensure the efficiency of getting high-quality solutions. Initially, a targeting model incorporating linear heat integration is adopted to reduce the superstructure and partially initialize the design model. Subsequently, in the synthesizing stage, a two-level global search procedure is devised, integrating adaptive exchanger adjustment with multi-start local optimization. The robustness and efficiency of the proposed approach are confirmed through the resolution of four large-scale problems within one hour, yielding improved results compared to the best-known existing solutions.

A thermal model for topology optimization in additive manufacturing: Design of support structures and geometry orientation

In this study, we present an efficient Topology Optimization (TO) approach designed to optimize support structures in metal Additive Manufacturing (AM), with a particular focus on Powder Bed Fusion (PBF) technology. The developed framework uses a purely thermal formulation to identify regions within the design that are susceptible to high heat concentrations. In the proposed modeling of the AM process, we postulate that new material layers are included in a partially built design that has already cooled to a controlled temperature. This aspect provides a layer-by-layer model of AM entirely local, enabling the building process parallelization and resulting in an algorithm with superior computational efficiency. Numerical results show the robustness of the proposed strategy, with the successful incorporation of support structures beneath overhanging surfaces and their effectiveness across a wide range of geometries and orientations. Furthermore, this framework is also applied to optimize the geometry orientation within the build chamber, further enhancing its applicability in the AM context.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Deep fuzzy nets approach for energy efficiency optimization in smart grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

An efficient bi-objective optimization approach for the optimal design of omnichannel supply chain distribution networks with sustainability

ABSTRACT This article studies a bi-objective omnichannel supply chain network design (SCND) problem, while simultaneously minimizing the overall supply chain (SC) cost and associated environmental emissions. A novel bi-objective mixed integer linear programming model is formulated, then an efficient optimization approach is proposed for the problem. Experimental studies on a case study and 210 random testing instances were conducted to evaluate the performance of the proposed model and approach. For the case study, 10 Pareto-optimal solutions were obtained within 1.9 s. For the random testing instances, the average number of Pareto-optimal solutions and computational time were 10.19 and 18.85 s, respectively. The overall results show that the proposed method is efficient and outperforms the adapted non-dominated sorting genetic algorithm II on test instances. Computational results indicate that this approach could assist decision makers in making good decisions, with considerable SC cost saving and carbon emission reductions in different situations.

Efficient Optimization approach in WSN using Multicast Forwarding Scheme in 6LowPAN Environment

The rapidly growing network of devices that are connected known as the Internet of Things, also referred to as IoT, is designed to collect and exchange data. These devices are integrated with sensors, software, and other technologies. An IPv6-based routing system called 6LowPAN was created especially for Internet of Things devices with little resources. However, because multicast packets must be replicated to every multicast group member, the conventional multicast forwarding technique in 6LowPAN may be wasteful. This can result in excessive energy usage and network traffic, particularly in large-scale Internet of Things implementations. An optimization process is presented here using a multicast forwarding scheme. In the proposed forwarding scheme, nodes can send multicast packets up and down at Low Power and Lossy networks over the 6LowPAN environment. Should the sink node be on the root node, it should immediately request that other nodes join the multicast messaging network of things. If otherwise, an ICMPv6 packet will be sent towards the root node in the Destination-oriented Directed-Acyclic Graphs (DODAG) tree, which expands the multicast messages in the first source's interest. In multi-bounce forward straight-line topology, when the DODAG network root is the source of multicast congestion, it exhibits a similar ratio of packet delivery and delay from end to end with a minimal memory overhead. When traffic via multicast originates from the most important rank node in a backward straight-line topology, this paper focuses on the efficiency of the multicast network at 6LowPAN.

Computation cost reduction in 3D shape optimization of nanophotonic components

Inverse design methodologies effectively optimize many design parameters of a photonic device with respect to a primary objective, uncovering locally optimal designs in a typically non-convex parameter space. Often, a variety of secondary objectives (performance metrics) also need to be considered before fabrication takes place. Hence, a large collection of optimized designs is useful, as their performance on secondary objectives often varies. For certain classes of components such as shape-optimized devices, the most efficient optimization approach is to begin with 2D optimization from random parameter initialization and then follow up with 3D re-optimization. Nevertheless, the latter stage is substantially time- and resource-intensive. Thus, obtaining a desired collection of optimized designs through repeated 3D optimizations is a computational challenge. To address this issue, a machine learning-based regression model is proposed to reduce the computation cost involved in the 3D optimization stage. The regression model correlates the 2D and 3D optimized structural parameters based on a small dataset. Using the predicted design parameters from this model as the initial condition for 3D optimization, the same optima are reached faster. The effectiveness of this approach is demonstrated in the shape optimization-based inverse design of TE0-TE1 mode converters, an important component in mode-division multiplexing applications. The final optimized designs are identical in both approaches, but leveraging a machine learning-based regression model offers a 35% reduction in computation load for the 3D optimization step. The approach provides a more effective means for sampling larger numbers of 3D optimized designs.