Optimization Algorithm Research Articles

Efficient Fault Warning Model Using Improved Red Deer Algorithm and Attention-Enhanced Bidirectional Long Short-Term Memory Network

The rapid advancement of industrial processes makes ensuring the stability of industrial equipment a critical factor in improving production efficiency and safeguarding operational safety. Fault warning systems, as a key technological means to enhance equipment stability, are increasingly gaining attention across industries. However, as equipment structures and functions become increasingly complex, traditional fault warning methods face challenges such as limited prediction accuracy and difficulties in meeting real-time requirements. To address these challenges, this paper proposes an innovative hybrid fault warning method. The proposed approach integrates a multi-strategy improved red deer optimization algorithm (MIRDA), attention mechanism, and bidirectional long short-term memory network (BiLSTM). Firstly, the red deer optimization algorithm (RDA) is enhanced through improvements in population initialization strategy, adaptive optimal guidance strategy, chaos regulation factor, and double-sided mirror reflection theory, thereby enhancing its optimization performance. Subsequently, the MIRDA is employed to optimize the hyperparameters of the BiLSTM model incorporating an attention mechanism. A predictive model is then constructed based on the optimized Attention-BiLSTM, which, combined with a sliding window approach, provides robust support for fault threshold identification. The proposed algorithm’s efficacy is demonstrated through its application to real-world gas-fired power plant equipment fault cases. Comparative analyses with other advanced algorithms reveal its superior robustness and accuracy in efficiently issuing fault warnings. This research not only provides a more reliable safeguard for the stable operation of industrial equipment but also pioneers a new avenue for the application of metaheuristic algorithms.

Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment.

To develop and implement a fully automatic iterative planning (AIP) system in the clinical practice, generating volumetric-modulated arc therapy plans combined with simultaneous integrated boost technique VMAT (SIB-VMAT) for locally advanced rectal cancer (LARC) patients. The designed AIP system aimed to automate the entire planning process through a web-based service, includingauxiliary structuregeneration,plan creation,field configuration,plan optimization,dose calculation, andplan assessment. The system was implemented based on theEclipse scriptingapplication programming interface and an efficientiterative optimization algorithm was proposed to reduce the required iterations in the optimization process. To verify the performance of the implemented AIP system, we retrospectively selected a total of 106 patients and performed dosimetric comparisons between the automatic plans (APs) and the manual plans (MPs), in terms of dose-volume histogram (DVH) metrics, homogeneity index (HI), and conformity index (CI) for different volumes of interest. The AIP system has successfully created 106 APs within clinically acceptable timeframes. The average planning time per case was 36.8±6.5 min, with an average iteration number of 6.8(±1.1) in plan optimization. Compared to MPs, APs exhibited better performance in the planning target volume conformity and hotspot control ( ). The organs at risk (OARs) sparing was significantly improved in APs, with mean dose reductions in the femoral heads, the bone marrow, and the SmallBowel-Avoid of 0.53Gy, 1.18Gy, and 1.00Gy, respectively ( ). Slight improvement was also observed in the urinary bladder and the small bowel . Additionally, quality variation between plans from different planners was observed in DVH metrics while the APs represented better plan quality consistency. An AIP system has been implemented and integrated into the clinical treatment planning workflow. The AIP-generated SIB-VMAT plans for LARC have demonstrated superior plan quality and consistency compared with the manual counterparts. In the meantime, the planning time has been reduced by the AIP approach. Based on the reported results, the implemented AIP framework has been proven to improve plan quality and planning efficiency, liberating planners from the laborious parameter-tuning in the optimization phase.

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

A dual-band dipole array antenna with fan-beam radiation for biomedical and telecommunications applications

This study introduces a new technique for achieving dual-band functionality in a printed dipole antenna with integrated balun feeding without additional elements. By employing an optimization algorithm, the fundamental parameters of the antenna are extracted, resulting in an antenna design that operates effectively in two distinct frequency bands of 2.4–2.5 and 5–6 GHz for WLAN/ISM and biomedical applications. By implementing the proposed design in a two-element array structure, the antenna successfully achieved a fan-beam radiation pattern in both frequencies. The array antenna exhibits fan-beam radiation patterns and peak gains of 6.90 dB and 7.84 dB, respectively, in two frequency bands: 2.06–2.52 GHz and 5.09–5.93 GHz. According to the proposed design, the array antenna has measured half-power beamwidths (HPBWs) of 38.34° and 25.14° in the two frequencies specified. The overall dimensions of the antenna are 1.100λ × 0.563λ × 0.004λ. This research design offers advantages such as smaller dimensions, higher gain, and a more straightforward structure compared to dual-band array antennas with fan beam radiation patterns.

Solving large-scale instances of the urban transit routing problem with a parallel artificial bee colony-hill climbing optimization algorithm

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl’s well-known instance and Mumford’s large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours.

Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators.

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10-18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.

A new optimization framework for piezoelectric actuators location identification

The study addresses piezoelectric actuator placement for active vibration control using an open-access framework that couples ANSYS and Python. It outlines a three-stage procedure: creating a finite element model in ANSYS APDL, using Python to determine the objective function, and applying a genetic algorithm optimization to find optimal actuator locations. Numerical simulations on plates and thin-walled conical structures demonstrate the effectiveness of the approach, showing it efficiently finds optimal placements with minimal search time and energy requirements. The study provides open-access codes for the framework, enhancing its accessibility and reproducibility.

Early quality prediction of complex double-walled hollow turbine blades based on improved whale optimization algorithm

Abstract The precision in forming complex double-walled hollow turbine blades significantly influences their cooling efficiency, making the selection of appropriate casting process parameters critical for achieving fine-casting blade formation. However, the high cost associated with real blade casting necessitates strategies to enhance product formation rates and mitigate cost losses stemming from the overshoot phenomenon. To address this challenge, we propose a machine-learning (ML) data-driven framework leveraging an enhanced whale optimization algorithm (WOA) to estimate product formation under diverse process conditions. Complex double-walled hollow turbine blades serve as a representative case within our proposed framework. We constructed a database using simulation data, employed feature engineering to identify crucial features and streamline inputs, and utilized a whale optimization algorithm-back-propagation neural network (WOA-BP) as the foundational ML model. To enhance WOA-BP's performance, we introduce an optimization algorithm, ICWOA-BP, incorporating cubic chaotic mapping adaptation. Experimental evaluation of ICWOA-BP demonstrated an average mean absolute error (MAE) of 0.001995 mm, reflecting a 36.21% reduction in prediction error compared to conventional models, as well as two well-known optimization algorithms (PSO, QANA). Consequently, ICWOA-BP emerges as an effective tool for early prediction of dimensional quality in complex double-walled hollow turbine blades.

Robot inverse kinematics solution for center selection battle royale optimization algorithm

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm, this paper proposes an improved battle royale optimization algorithm based on chaos mapping, center selection and elite adaptive strategy. Through benchmark function testing, compared to particle swarm optimization algorithm, whale optimization algorithm, and the battle royale optimization algorithm, the improved algorithm significantly reduces time complexity and notably enhances convergence precision, speed, and stability. The improved algorithm is applied to the inverse kinematics problem of robots. The accuracy and The stability of the improved algorithm is better than those of the traditional battle royale optimization algorithm, it demonstrates superior solving precision and stability, proving its practicality and potential for development in solving robot inverse kinematics problems.

Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F‐Race

Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single‐objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi‐criteria case. We conduct experiments with a swarm of e‐puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or ‐norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F‐race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated.

A New Hybrid Improved Arithmetic Optimization Algorithm for Solving Global and Engineering Optimization Problems

The Arithmetic Optimization Algorithm (AOA) is a novel metaheuristic inspired by mathematical arithmetic operators. Due to its simple structure and flexible parameter adjustment, the AOA has been applied to solve various engineering problems. However, the AOA still faces challenges such as poor exploitation ability and a tendency to fall into local optima, especially in complex, high-dimensional problems. In this paper, we propose a Hybrid Improved Arithmetic Optimization Algorithm (HIAOA) to address the issues of susceptibility to local optima in AOAs. First, grey wolf optimization is incorporated into the AOAs, where the group hunting behavior of GWO allows multiple individuals to perform local searches at the same time, enabling the solution to be more finely tuned and avoiding over-concentration in a particular region, which can improve the exploitation capability of the AOA. Second, at the end of each AOA run, the follower mechanism and the Cauchy mutation operation of the Sparrow Search Algorithm are selected with the same probability and perturbed to enhance the ability of the AOA to escape from the local optimum. The overall performance of the improved algorithm is assessed by selecting 23 benchmark functions and using the Wilcoxon rank-sum test. The results of the HIAOA are compared with other intelligent optimization algorithms. Furthermore, the HIAOA can also solve three engineering design problems successfully, demonstrating its competitiveness. According to the experimental results, the HIAOA has better test results than the comparator.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Economical design strategies for reinforced concrete one-way slabs: a parametric approach considering material strength and rebar size

Despite the rapid advancements in optimization algorithms for economical reinforced concrete (RC) structures, their application to RC slabs has been significantly limited. The primary constraint is slab depth, governed only by deflection control criteria, which impedes further depth reduction—a key factor in cost optimization. The deflection check relies solely on one design parameter—span length, which is often fixed. This constraint limits the effectiveness of even advanced algorithms, which have consistently failed to deliver substantial cost savings in slab design. To address this issue, this study presents a detailed parametric investigation of three key cost-reducing factors in RC one-way slabs: steel strength, concrete strength, and reinforcing bar sizes, in accordance with the ACI 318–19 code. By bypassing algorithmic constraints, the study formulates optimal design strategies through extensive manual design iterations, providing novel trend-based solutions for cost-effective slab design. The findings indicate that for lower live loads, grade 280 steel offers a 20–30% cost reduction compared to higher-grade steel, despite the latter's strength advantage. However, for higher loads, grade 420 or 500 is recommended. Additionally, optimal bar sizes and concrete strengths are recommended for varying load conditions, offering practical design strategies. This approach provides designers with actionable insights to achieve cost-efficient designs without relying on traditional optimization algorithms.

A Novel Deep Learning Framework Enhanced by Hybrid Optimization Using Dung Beetle and Fick’s Law for Superior Pneumonia Detection

Pneumonia is an inflammation of lung tissue caused by various infectious microorganisms and noninfectious factors. It affects people of all ages, but vulnerable age groups are more susceptible. Imaging techniques, such as chest X-rays (CXRs), are crucial in early detection and prompt action. CXRs for this condition are characterized by radiopaque appearances or sometimes a consolidation in the affected part of the lung caused by inflammatory secretions that replace the air in the infected alveoli. Accurate early detection of pneumonia is essential to avoid its potentially fatal consequences, particularly in children and the elderly. This paper proposes an enhanced framework based on convolutional neural network (CNN) architecture, specifically utilizing a transfer-learning-based architecture (MobileNet V1), which has outperformed recent models. The proposed framework is improved using a hybrid method combining the operation of two optimization algorithms: the dung beetle optimizer (DBO), which enhances exploration by mimicking dung beetles’ navigational strategies, and Fick’s law algorithm (FLA), which improves exploitation by guiding solutions toward optimal areas. This hybrid optimization effectively balances exploration and exploitation, significantly enhancing model performance. The model was trained on 7750 chest X-ray images. The framework can distinguish between healthy and pneumonia, achieving an accuracy of 98.19 ± 0.94% and a sensitivity of 98 ± 0.99%. The results are promising, indicating that this new framework could be used for the early detection of pneumonia with a low cost and high accuracy, especially in remote areas that lack expertise in radiology, thus reducing the mortality rate caused by pneumonia.

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Research on IP Node Port Openness Prediction Method Based on PSO-CatBoost

The development of network measurement technologies has greatly increased the speed of network scans, but it also poses risks for the stability of the scanned networks. How to reduce probing traffic and enhance the effectiveness of probing has become a new research issue. In this paper, we utilize network measurement and machine learning techniques, leveraging public interfaces from network mapping platforms to construct a dataset with 44 feature dimensions. By combining the categorical boosting (CatBoost) model with the particle swarm optimization (PSO) algorithm for heuristic optimization, we propose a host port openness prediction model that integrates the PSO algorithm and the CatBoost model. Through comparisons with various machine learning models, the effectiveness of our proposed model was validated. Using this model in network scanning can save approximately 65% of bandwidth on average, effectively reducing the impact on the probed network.

Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises

BackgroundThe transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis–Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. ResultsWe quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis–Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis–Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm.ConclusionsAltogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

Optimal Curve Fitting for Serial Chain in Six-Crease Origami Unit

Abstract Origami-inspired structures have been widely used in aerospace and robotics for three-dimensional (3D) symmetrical configurations using crease-symmetrical origami basic patterns. These patterns offer advantages in repeatable and systematic modeling and mass production. However, few studies have focused on 3D non-symmetrical structures using symmetrical origami basic patterns due to their structure complexity, limiting their application. Therefore, we aim to analyze the folding behavior in 3D non-symmetrical structures using a 6-crease symmetry origami base pattern. To achieve this goal, we first focus on behavior in a two-dimensional (2D) plane. This paper presents a scheme for the behavior of origami units with an optimal curve-fitting algorithm. The curve can be any 2D space curve. The fitting curve, constructed by numerical analysis and an optimal approaching scheme, can satisfy error requirements and retain foldable origami unit features. The paper verifies the feasibility of the curve-fitting scheme by presenting two curve examples, including a quadratic curve and a sin wave function. The results show that the fitting error is reduced by 99% when no boundary conditions are applied. This research provides valuable insights into understanding origami unit kinematic optimization through forward and inverse kinematics. It offers potential applications in the engineering design of foldable structures and precision origami-inspired mechanism, thereby opening avenues for further exploration of complex origami structures and their applications in emerging technologies.

Forecasting urban water demand using different hybrid-based metaheuristic algorithms’ inspire for extracting artificial neural network hyperparameters

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms’ behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing. Each model performed adequately in simulating the fundamental dynamics of monthly urban water demand as it relates to meteorological variables, proving that they were all successful. Statistical fitness measures showed that PSOGA-ANN outperformed competing algorithms.

Reinforcement Learning-Based Optimal Hull Form Design with Variations in Fore and Aft Parts

Abstract With recent advancements in artificial intelligence technology, various studies are being conducted in the shipbuilding industry. Traditionally, hull form variation methods have relied on the intuition and expertise of designers, leading to inconsistent results and unintended changes in the ship's main dimensions depending on the designer's competence. Moreover, the iterative process of design variation and analysis to derive the optimal hull form is both costly and time-consuming. To address these issues, this study proposes an optimal hull design technique utilizing reinforcement learning, a type of unsupervised learning in machine learning. Reinforcement learning allows the model to learn from past policies by recording and accumulating the rewards associated with various actions taken by an agent in a specific environment. In this study, after calculating the main parameters of the ship, the agent defines a state representing hull information and performs local transformations of the bow and stern. The reward of reinforcement learning is defined as the change in total resistance due to the hull deformation, constrained by limiting the tolerance of the ship's prismatic coefficient (CP) and longitudinal center of buoyancy (LCB). In this study, the problem is solved by comparing the proximal policy optimization (PPO) algorithm and the deep deterministic policy gradient (DDPG) algorithm to find the best deep reinforcement learning (DRL) model for the hull optimization problem. The results were compared with the genetic algorithm and speed-constrained multi-objective particle swarm optimization (SMPSO), and the optimal hull resistance values were less different, but the time of the reinforcement learning model was five times shorter.