Optimization-based Algorithm Research Articles

Optimization Study of Drainage Network Systems Based on the SWMM for the Wujin District, Changzhou City, Jiangsu Province, China

This study addresses the persistent issue of urban waterlogging in Wujin District, Changzhou City, Jiangsu Province, using a comprehensive approach integrating an optimized drainage network and low-impact development (LID) measures. Utilizing the Storm Water Management Model (SWMM), calibrated with extensive hydrological and hydraulic data, the model was refined through genetic algorithm-based optimization to enhance drainage efficiency. Key results indicate a substantial reduction in the average duration of waterlogging from 7.43 h to 3.12 h and a decrease in average floodwater depth from 21.27 cm to 8.65 cm. Improvements in the drainage network layout, such as the construction of new stormwater mains, branch drains, and rainwater storage facilities, combined with LID interventions like permeable pavements and rain gardens, have led to a 56.82% increase in drainage efficiency and a 63.88% reduction in system failure rates. The implementation effectively minimized peak flood flow by 25.38%, reduced runoff, and improved groundwater recharge and rainwater utilization. The proposed solutions offer a replicable, sustainable framework for mitigating flooding in urban environments, enhancing ecological resilience, and ensuring the safety and quality of urban life in densely populated areas.

Optimization Tools for the Design of Meta-Covers for Linear Antenna with Beam- and Null-Steering Capabilities

This paper investigates the optimization of cylindrical metasurface meta-covers designed to enable beamforming capabilities in single linear antennas. This study focuses on the development and application of advanced optimization tools to tailor the electromagnetic response of these metasurfaces, enabling precise control over the radiation patterns of the antenna. In particular, we develop a genetic algorithm-based optimization tool, which achieves precise manipulation of the main beam direction and null placement in the radiation pattern. The results further expand the applications of metasurface-based meta-covers in enhancing the functionality of dipole antennas in various communication and sensing systems.

Development of a Hybrid Fuzzy Geographically Weighted K-Prototype Clustering and Genetic Algorithm for Enhanced Spatial Analysis: Application to Rural Development Mapping

Introduction/Main Objectives: Clustering methods are crucial for geodemographic analysis (GDA) as they enable a more accurate and distinct characterization of a region. This process facilitates the creation of socio-economic policies and contributes to the overall advancement of the region. Background Problems: The fuzzy geographically weighted clustering (FGWC) method, which is a GDA technique, primarily handles numerical data and is prone to being stuck in local optima. Novelty: This study proposed two novel clustering methodologies: fuzzy geographically weighted k-prototypes (FKP-GW) and its hybrid clustering model, which combines genetic algorithm-based optimization (GA-FKP-GW). Research Methods: This research conduct simulation study comparing two of the proposed clustering method. For the empirical application, this study applied clustering technique using the official Village Potential Survey of Temanggung, Indonesia. Finding/Results: The evaluation results of experiments conducted on simulated data and study cases indicate that the proposed method yields distinct clustering results compared to the previous method while being comparably efficient. The empirical application identifies four distinct groups from the clustered villages, each displaying unique characteristics. The results of our research have the potential to benefit the development of the GDA method and assist the local government in formulating more effective development policies.

Optimization-Based Energy Management Algorithm for 2-Stroke Hybrid Ship with Controllable Pitch Propeller

This paper examines the fuel consumption savings of a hybrid ship powertrain with 2-stroke main engine by implementing a novel adaptive equivalent consumption minimization strategy that utilizes a controllable pitch propeller. A non-hybrid powertrain model was developed as a demonstrator and real-world data were used for fuel consumption and efficiency maps. The baseline powertrain model was extended to a hybrid by introducing a shaft generator, a battery, a controllable pitch propeller, and the supervisory control algorithm. The potential benefits of the proposed powertrain are examined over different operation phases including port stay, open sea sailing, and port approach. The result showed that the energy efficiency gains can reach up to 6% under the open sea sailing phase. Furthermore, the controllable pitch propeller offers additional energy efficiency benefits of 2% under the port approach phase, utilizing the proposed algorithm. If the proposed powertrain is produced and the implemented algorithm is adopted, this could lead to substantial carbon dioxide emissions and fuel consumption savings at sea.

Numerical Design Structure Matrix–Genetic Algorithm-Based Optimization Method for Design Process of Complex Civil Aircraft Systems

In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept of overlap coefficient is proposed to further sort out the forward and backward logical relationships between design tasks and the civil aircraft system design process optimization model based on a numerical design structure matrix. The algorithm NSGA-II is improved and verified with the flight control system design as a case study. The results show that the proposed method can effectively improve the efficiency of complex system design and provide technical support for the optimization of the design process of complex civil aircraft systems.

Memetic algorithm-based optimization of hybrid forecasting systems for multivariate time series

Memetic algorithm-based optimization of hybrid forecasting systems for multivariate time series

Genetic algorithm-based optimal design for fluidic artificial muscle (FAM) bundles

In this paper, we present a design optimization framework for a fluidic artificial muscle (FAM) bundle subject to geometric constraints. The architecture of natural skeletal muscles allows for compact actuation packaging by distributing a substantial number of actuation elements or muscle fiber motor units, which are to be arranged, oriented, and sized in various formations. Many researchers have drawn inspiration from these natural muscle architectures to assist in designing soft robotic systems safe for human-robot interaction. Although there are known tradeoffs identified between different muscle architectures, this optimization framework offers a method to map these architectural tradeoffs to soft actuator designs. The actuation elements selected for this study are FAMs or McKibben muscles due to their inherent compliance, cheap construction, high force-to-weight ratio, and muscle-like force-contraction behavior. Preceding studies analytically modeled the behavior of arranging FAMs in parallel, asymmetrical unipennate, and symmetrical bipennate topologies inspired by the fiber architectures found in human muscle tissues. A more recent study examined the implications of spatial constraints on bipennate FAM bundle actuation and found that by careful design, a bipennate FAM bundle can produce more force, contraction, stiffness, and work output than that of a parallel FAM bundle under equivalent spatial bounds. This multi-objective genetic algorithm-based optimization framework is used to realize desirable topological properties of a FAM bundle for maximum force and stroke for a given spatial envelope. The results help identify tradeoffs to inform design decisions based on the force and stroke demand from the desired operating task. This study further demonstrates how the desirable topological properties of the optimized FAM bundle change with different spatial bounds.

Genetic algorithm-based optimization of helical gear pair with non-standard center distances: validated through FEA and strain gauge technique

Genetic algorithm-based optimization of helical gear pair with non-standard center distances: validated through FEA and strain gauge technique

Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power

In 2022, global coal combustion contributed significantly to global pollution, producing 15.22 billion metric tons of carbon dioxide (CO2). This research addresses the urgent challenge of mitigating CO2 emissions in Indian coal power plants by strategically deploying solar photovoltaic (PV) systems and integrating demand response mechanisms. The imperative to reduce greenhouse gas emissions from coal-based electricity generation underscores the critical context of climate change. Emphasizing the vital role of integrating renewable energy-based distributed generators into the existing coal infrastructure, this study positions solar PV technology as a promising solution. Optimal solar PV system allocation is achieved through the implementation of the genetic algorithm technique. Factors such as solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation are considered in this process. The primary research objective is twofold: to minimize CO2 emissions and maximize the integration of solar PV systems while mitigating power losses. The proposed approach considers the intermittent nature of solar power and the dynamic characteristics of demand. Rigorous testing on an IEEE 33-bus system powered by the studied coal power plant reveals a substantial 29.31% reduction in CO2 generation following the implementation of the proposed strategy. This research represents a decisive step towards fostering a more sustainable and environmentally friendly energy landscape. Our study's outcomes offer valuable insights for policymakers and stakeholders in the energy sector, providing a robust foundation for the advancement of environmentally conscious practices within the coal power industry.

An $\ell _{0}$-Norm Optimization-Based Algorithm for Robust and Efficient MIMO Localization

An $\ell _{0}$-Norm Optimization-Based Algorithm for Robust and Efficient MIMO Localization

Codesign of transmit waveform and reflective beamforming for active reconfigurable intelligent surface-aided MIMO ISAC system

This article discusses the active reconfigurable intelligent surface (ARIS)-aided integrated sensing and communication (ISAC) system for non-line-of-sight (NLoS) target sensing in cluttered environments while performing multi-user communication. To optimize sensing and communication performance simultaneously, we jointly design the shared transmit waveform, ARIS reflection coefficients and radar receive filter by using the multi-user interference and the reciprocal of radar output signal-to-interference-plus-noise ratio as metrics. Limited by practical requirements, the transmit waveform suffers from constant modulus or total energy constraints and the ARIS is subject to both maximum power and amplification gain constraints. Based on these considerations, the proposed codesign is formulated into a nonconvex constrained fractional function minimization problem. To tackle it effectively, we first translate the fractional objective into an integral form by employing Dinkelbach transform and then propose an alternating optimization-based algorithm, where the transmit waveform and ARIS reflection coefficients are respectively optimized by the customized algorithms based on the consensus alternating direction method of multipliers, and the receive filter has a closed-form optimal solution. Numerical results demonstrate that the ARIS-aided ISAC concurrently achieve superior NLoS sensing and communication performance to passive reconfigurable intelligent surface-aided and traditional ISACs in cluttered environments, regardless of waveform constraints and sensing-communication trade-off factor.

New Building Design in historical environment: Production process with optimization-based algorithm

This study proposes a model for early-stage design optimization of newly constructed buildings in the historical environment, using the typomorphology-shape grammar method and NSGA-II genetic algorithm. Rhino, Grasshopper, Wallacei, and Sketchup platforms are used in the model. The street silhouette of the sample historical environment was analyzed using the typomorphology method and transformed into various rules using the shape grammar method. An optimization-based production for new designs was achieved with the design parameters created using the rule sets obtained here. Multiple conflicting goals and five different optimization packages classifying these goals were used to avoid restricting variations by focusing on the optimal solution. As a result of the optimizations, many design alternatives suitable for the silhouette of the historical environment were produced. With this model, it has been shown that many design alternatives can be produced simultaneously for a new building to be built in a historical environment and that it is essential to generate ideas at the early stage of design.

Mixed precision quantization of silicon optical neural network chip

In recent years, the field of neural network research has witnessed remarkable advancements in various domains. One of the emerging approaches is the integration of photonic computing, which leverages the unique properties of light for ultra-fast information processing. In this article, we establish a mixed precision quantization model to silicon-based optical neural networks and evaluates their performance on the MNIST and Fashion-MNIST datasets. Through a genetic algorithm-based optimization process, we achieve significant parameter compression while maintaining competitive accuracy. Our findings demonstrate that with an average quantization bitwidth of 4.5 bits on the MNIST dataset, we achieve an impressive 85.94% reduction in parameter size compared to traditional 32-bit networks, with only a marginal accuracy drop of 0.65%. Similarly, on the Fashion-MNIST dataset, we achieve an average quantization bitwidth of 5.67 bits, resulting in an 82.28% reduction in parameter size with a slight accuracy drop of 0.8%.

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Modelling and analysis of a fractional-order epidemic model incorporating genetic algorithm-based optimization

Modelling and analysis of a fractional-order epidemic model incorporating genetic algorithm-based optimization

Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator

An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.

Multi-Scale Candidate Fusion and Optimization-Based 3D Object Detection Algorithm

To address the issues of target omission and the inclusion of a large number of background points in keypoint sampling for point cloud-based object detection, an improved algorithm based on the PV-RCNN network is introduced. This approach employs both a regional proposal fusion network and weighted Non-Maximum Suppression (NMS) to merge proposals generated at various scales while eliminating redundancy. A segmentation network is utilized to segment foreground points from the original point cloud, and object center points are identified based on these proposals. Gaussian density functions are employed for regional density estimation, which assigns different sampling weights to solve the problem of difficult sampling in sparse areas. Experimental evaluations on the KITTI dataset indicate that the algorithm enhances the average precision at medium difficulty levels by 0.39%, 1.31%, and 0.63%for cars, pedestrians, and cyclists, respectively. Generalization experiments were also conducted on the Waymo dataset. The results suggest that the introduced algorithm achieves higher accuracy compared to most of the existing 3D object detection networks.

Development of a geothermal-driven multi-output scheme for electricity, cooling, and hydrogen production: Techno-economic assessment and genetic algorithm-based optimization

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Vibration attenuation of dual periodic pipelines using interconnected vibration absorbers

Pipelines are crucial elements in various engineering applications. However, unexpected vibrations from different sources can jeopardize the operational performance and potentially damage the piping structures and other connected units. This study explores flexural wave propagation and attenuation characteristics of the pipes supported periodically on a rack. Also, a specific case of a rack with infinite stiffness (simple support) is investigated. The dispersion relations pertinent to pipe rack scenario is obtained through transfer matrix method (TMM) in conjunction with Floquet-Bloch’s theorem, and the accuracy of ensuing band gaps (BGs) are confirmed through finite element (FE) models. Following this, the modal analysis is performed to ascertain the minimum number of unit-cells necessary for the FE model to accurately mimic the attenuation and propagation characteristics of the corresponding infinite structure. The findings indicate that a pipe supported on rack displays resonance and Bragg-type BGs, arising from local resonance and spatial periodicity, respectively, while the pipe on simple support exhibits only Bragg-type BGs. To control low-frequency vibrations in dual pipelines placed on the rack, a novel configuration using interconnected dynamic vibration absorbers (DVAs) is proposed. The DVA consisting of two spring-damper units along with a mass is connected across the center of each span of the two pipelines. The proposed DVA is designed via genetic algorithm-based optimization. It was found that the designed DVA significantly reduces the vibration of the two pipelines. The performance of DVAs improves with an increase in the mass ratio. Additionally, the performance of pipes connected with conventional tuned mass damper (TMD) is evaluated and compared with the proposed DVA. The effectiveness of DVAs is also verified by employing a white Gaussian noise as input. The proposed DVAs are efficient in scenarios involving multiple pipes within a rack, leveraging other pipe’s mass, stiffness, and damping properties to mitigate vibrations in the considered pipes.

Metaheuristic (Ant Colony Optimization) Algorithm-Based Optimization of a Circular Shaped Patch Antenna for Medical Purposes

Metaheuristic (Ant Colony Optimization) Algorithm-Based Optimization of a Circular Shaped Patch Antenna for Medical Purposes