Earticle

In this research, we develop and suggest branch and bound algorithms for a two-machine permutation flowshop scheduling problem with the objective of minimizing makespan. In this scheduling problem, after each job is operated on the machine 1 (first machine), the job has to start its second operation on machine 2 (second machine) within its corresponding limited waiting time. In addition, each job has its corresponding ready time at the machine 1. For this scheduling problem, we develop various dominance properties and three lower bounding schemes, which are used for the suggested branch and bound algorithm. In the results of computational tests, the branch and bound algorithms with dominance properties and lower bounding schemes, which are suggested in this paper, can give optimal solution within shorter CPU times than the branch and bound algorithms without those. Therefore, we can say that the suggested dominance properties and lower bounding schemes are efficient.

Numerous cities developed in the 20th century have a Water Distribution Network(WDN) with decade-old pipes. Aging pipelines can leak with lower the water revenue ratio, thus resulting in waste and economic losses. In this study, new methods for localizing leakage in WDNs are proposed and tested. Two meta-heuristic methods based on Harmony Search(HS) and Genetic Algorithm(GA) are developed to detect leakage of WDNs through an evaluation of emitter coefficients. The old town of G-Town in South Korea has a 50-year-old WDN and a low water revenue ratio. High quality field measurements in G-Town allowed detailed testing of optimization methods based on emitter coefficients for leakage detection. As a result, both GA and HS yielded comparable outputs. The HS method performed with slightly better accuracy in minimizing the objective function than GA after about 1,000 iterations. Leakage detection becomes fairly accurate after approximately 30 minutes of optimization and pipe networks with more than 100 pipes and 50 nodes require more iterations and calculation time.

The efficient deployment of robot-based manufacturing systems is frequently hindered by the substantial time required for programming collision-free robot paths during the commissioning process. This challenge involves intensive tasks such as teach-in, offline programming, and subsequent path optimization. To dramatically accelerate this critical stage, the industry needs an automatic and intelligent path planning system. This work introduces a novel system designed for the autonomous path planning of industrial robots. We conduct an explicit comparison between samplingbased methods such as probabilistic roadmaps (PRM) and rapidly exploring random Trees (RRT), and computational intelligence (CI) based methods, particularly genetic algorithms. Our findings demonstrate the potential for these advanced techniques to drastically reduce robot deployment time.

This paper proposes a prompt–optimization framework for generating style–consistent game images using Stable Diffusion XL. Given a reference game item image, the system first extracts an initial prompt using a vision–language captioner and a domain–specific prompt bank. The extracted prompt is converted into a list of noun-like elements, and a genetic algorithm searches for compact combinations of these elements under dual gating based on SSIM and CLIP scores. The best combinations are treated as a “style template” that can reproduce the reference image with high structural and semantic similarity. We then investigate whether this template can be reused when the main object is changed while preserving the original visual style. Experiments on fantasy-style item images show that the framework reconstructs reference images using only 8–9 automatically discovered prompt elements, and that changing the main object token together with associated detail elements yields image sets that share a consistent visual style. In contrast, naïvely replacing only the main object token often produces visually ambiguous or stylistically inconsistent images. These results demonstrate that combining automatic prompt extraction from images with evolutionary optimization provides a concrete example of style–preserving prompt design for game item image generation.

Advances in digital tools and building structure technologies have enabled more flexible architectural design, with AI-based performance design gaining considerable attention as a new design methodology. Stadium design must consider the two primary elements of sports events: athletes and spectators. Given that the facade of a stadium directly impacts solar energy efficiency, it is essential to incorporate environmental performance considerations from the initial design phase. This study employs an AI-based Generative Design process to generate a facade form that efficiently manages solar radiation and daylight, satisfying two conflicting performance objectives: max- imizing sunlight for turf growth in the pitch zone and minimizing direct sunlight exposure in the stadium seating zone. The optimal solution derived ranks 331st for pitch zone sunlight and 408th for stadium seating sunlight out of a dataset of 1,000 models. While this solution does not represent the absolute best for either individual objective, it is evaluated as the most balanced alternative, achieving the goal of maximizing sunlight in the pitch zone and minimizing it in the seating zone

In this study, a welding heat source model was presented and verified during fiber laser welding. The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. It consists of a total of 12 parameters, and an optimization algorithm was used to find them. As optimization algorithms, adaptive simulated annealing, multi island genetic algorithm, and Hooke-Jeeves technique were applied for comparative analysis. The parameters were found by comparing the temperature distribution when the STS304L was bead on plate welding and the temperature distribution derived through finite element analysis, and all three models were able to derive a model with similar trends. However, there was a deviation between parameters, which was attributed to the many variables. It is expected that a more clear welding heat source model can be derived in subsequent studies by giving a guide to the relational expression and range between variables and increasing the temperature measurement point, which is the target value.

The importance of eco-friendly architectural design has become an essential design element along with the global climate crisis. Nevertheless, eco-friendly considerations are generally made in the mid to late stages of the architectural design process, and in particular, there is a high tendency to focus on consideration of architectural facilities or facade design rather than determining the overall shape, such as the layout, height, or orientation of the building. However, as mentioned above, the decisions made in the early design process often determine the actual performance of the building, so more advanced research is needed on ways to increase the energy performance of buildings in the early design stage. An algorithm was designed so that the eco-friendly design techniques derived through this study can be universally applied not only to specific sites but also to unspecified sites. Through the two proposed stages - placement, height and orientation - it was possible to derive an optimized building shape in the early design stage, and thus it can be said to be a technique that enables a high level of energy savings throughout the entire life cycle of the building.

양자 컴퓨팅 발전에 따른 Shor 알고리즘 및 Grover 알고리즘의 등장으로 인해 기존 공개키암호 알고리즘이 위협받 고 있다. 이에 따라 미국 국립표준기술연구소에서는 양자내성암호 표준화 공모전을 통하여, 양자 컴퓨팅 발전으로 인한 보안 위협에 대비하고 있다. 표준화 공모전을 통해 선정된 4종의 표준화 대상 양자내성암호들은 보안성 및 민 첩성 측면에서 각기 다양한 방식으로 연구되고 있으며, 이 중 격자 기반의 전자서명 알고리즘인 CRYSTALSDilithium은 다항식 곱셈을 위하여 NTT 연산을 이용하고 있으나, 최근 희소 다항식 곱셈을 적용하여 알고리즘 성 능을 향상시킬 수 있다는 연구 결과가 발표된 바 있다. 해당 논문에서는 Cortex-M4, Apple M2 기반 실험 환경을 구축하여 검증을 진행했으나, 다른 ARM 계열 CPU 환경에서도 속도를 향상시킬 수 있는지에 대한 성능 검증은 이 루어지지 않았다. 이에, 본 논문에서는 다른 ARM 계열 CPU 환경에서도 희소 다항식 곱셈을 적용할 경우에 대한 CRYSTALS-Dilithium의 성능을 검증하고자 Dilithium2, Dilithium3, Dilithium5 알고리즘 각각에 대해 세 가지 코드(레퍼런스 코드, 희소 다항식 곱셈 알고리즘, SIMD를 적용한 희소 다항식 곱셈 알고리즘)를 구현하고 이 에 대한 성능을 검증하였다.

The emergence of Shor and Grover algorithms following the development of quantum computing is threatening existing public key encryption algorithms. As a result, the National Institute of Standards and Technology (NIST) is preparing for security threats caused by the development of quantum computing through a competition for standardization of post quantum cryptography. The four types of standardized post quantum cryptography. selected through the standardization contest are being studied in various ways in terms of security and agility, and CRYSTALS-Dilithium, a grid- based digital signature algorithm, uses NTT operation for polynomial multiplication, but recently a research result has been published that shows that algorithm performance can be improved by applying sparse polynomial multiplication [8]. In this paper, Cortex-M4 and Apple M2 based experimental environments were built and verified, but performance verification has not been made on whether speed can be improved in other ARM-based CPU environments. Therefore, in this paper, three codes (reference code, sparse polynomial multiplication algorithm, and sparse polynomial multiplication algorithm with SIMD) were implemented and their performance was verified for each of the Dilithium2, Dilithium3, and Dilithium5 algorithms to verify the performance of CRYSTALSDilithium when applying sparse polynomial multiplication in other ARM-based CPU environments.

Case-based reasoning (CBR) is a reasoning technique that reuses past cases to find a solution to the new problem. It often shows significant promise for improving effectiveness of complex and unstructured decision making. It has been applied to various problem-solving areas including manufacturing, finance and marketing for the reason. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most of the previous studies on CBR have focused on the similarity function or optimization of case features and their weights. According to some of the prior research, however, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. In spite of the fact, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the novel approach to Korean stock market. Experimental results show that the GA-optimized k-NN approach outperforms other AI techniques for stock market prediction.

본 숏폼 광고의 AI 추천 알고리즘 특성이 광고 피로도와 광고 회피에 미치는 영향을 실증적 으로 분석하였다. 디지털 미디어 환경에서 급성장하고 있는 숏폼 콘텐츠 플랫폼은 AI 기 반 추천 알고리즘을 통해 개인화된 광고를 제공하고 있으나, 이러한 개인화가 오히려 광고 피로 도와 회피 행동을 유발할 수 있다는 문제의식에서 출발하였다. 본 연구는 확증편향, 지각된 침입 성, 지각된 최적화, AI 알고리즘 신뢰성을 주요 독립변인으로 설정하고, 광고 피로도를 매개변인 으로 하여 광고 회피에 미치는 영향을 검증하였다. 분석 결과, 지각된 침입성은 광고 피로도에 강한 정적 영향을, 확증편향과 AI 알고리즘 신뢰성은 광고 피로도에 부적 영향을 미치는 것으로 나타났다. 지각된 최적화는 광고 피로도에는 유의한 영향을 미치지 않았으나 광고 회피에는 직 접적인 부적 영향을 미쳤다. 광고 피로도는 광고 회피에 강한 정적 영향을 미쳤으며, 매개효과 분석 결과 지각된 침입성과 AI 알고리즘 신뢰성이 광고 피로도를 매개로 광고 회피에 유의한 영 향을 미치는 것을 확인하였다. 본 연구는 숏폼 광고 환경에서 AI 추천 알고리즘의 부정적 효과 메커니즘을 체계적으로 규명한 첫 연구으로 디지털 마케팅 이론 확장에 기여하며, 실무적으로는 광고 피로도와 회피를 완화하기 위한 알고리즘 최적화 전략을 제시한다.

This study empirically analyzes the impact of AI recommendation algorithm characteristics on ad fatigue and ad avoidance in short-form advertising. While short-form content platforms experiencing rapid growth in the digital media environment provide personalized advertisements through AI-based recommendation algorithms, this research originated from the recognition that such personalization may paradoxically induce ad fatigue and avoidance behaviors. The study established confirmation bias, perceived intrusiveness, perceived optimization, and AI algorithm trustworthiness as major independent variables, with ad fatigue as a mediating variable to examine their effects on ad avoidance. The results revealed that perceived intrusiveness had a strong positive effect on ad fatigue, while confirmation bias and AI algorithm trustworthiness had negative effects on ad fatigue. Perceived optimization did not significantly affect ad fatigue but had a direct negative effect on ad avoidance. Ad fatigue had a strong positive effect on ad avoidance. The mediation analysis confirmed that perceived intrusiveness and AI algorithm trustworthiness significantly influenced ad avoidance through ad fatigue as a mediator. This study represents the first systematic investigation of the negative effect mechanisms of AI recommendation algorithms in short-form advertising environments, contributing to the expansion of digital marketing theory. Practically, it provides algorithm optimization strategies to mitigate ad fatigue and avoidance.

Test functions play an important role in validating and comparing the performance of optimization algorithms. The test functions should have some diverse properties, which can be useful in testing of any new algorithm. The efficiency, reliability and validation of optimization algorithms can be done by using a set of standard benchmarks or test functions. For any new optimization, it is necessary to validate its performance and compare it with other existing algorithms using a good set of test functions. Optimization problems are widely used in various fields of science and technology. Sometimes such problems can be very complex. Particle Swarm Optimization is a stochastic algorithm used for solving such optimization problems. This paper transplants some of the test functions which can be used to test the performance of Particle Swarm Optimization (PSO) algorithm, in order to improve its performance and have better results. Different test functions can be used for different types of problems. These test functions have a specific range and values, which can be applied in different situations. These functions, when applied to the PSO algorithm, can give the better comparison of results. The test functions that have been the most commonly adopted to assess performance of PSO-based algorithms and details of each of them are provided, such as the search range, the position of their known optima, and other relevant properties.

Transport with minimum time cost and distance remains to be an important research area in intelligent transport systems. Shortest path algorithms are primary methods to address simplified problems, which could not be well applied in high-dimensional real situations. We realized the minimum cost and maximum flow result via classical iterative algorithm based on graph theory, adjacency matrix is well applied to express the relationship between transport nodes, a topological sorting transport map is adopted to verify these approaches.

Decaying with the increasing of signal propagation distance, Received Signal Strength (RSS) is used in the wireless localization due to its low cost and easily implementation. When the transmit power is unavailable, two convex optimization algorithms including semi definite programming (SDP), second order cone and semi definite programming (SOC/SDP) are designed to estimate the source locations by relaxing the non-convex problem as convex optimization. The corresponding Cramér-Rao lower bound (CRLB) of the problem is derived. The simulations demonstrate that the SOC/SDP algorithm provides the similar accuracy performance compared with the SDP algorithm. However the computational complexity of SOC/SDP is lower than that of the SDP due to the less variables and equality constraints. When perfect knowledge of the path loss exponent is available, the simulations also show that the accuracy performance of the proposed convex optimization algorithms degrades as the path loss exponent increases.

Without multi query optimization, Relational Database Management System for online and analytical decision support systems would have been inefficient and hence unpractical. It is an expensive process because it relies at a great extent on evaluating the different plans (access paths) and choosing an optimal one among them. In Multi Query Optimization, queries are executed in batches and there were many different algorithms acted in such way that, in case some queries have a common sub-expression such a sub- expression is executed once and the output shared. We studied the basic multi query optimization algorithms including Basic Volcano, Volcano-SH and Volcano RU, identified their strengths and weaknesses and recommend strategies for developing new improved multi query optimization algorithm so as to reduce weaknesses and integrate strengths of the different basic multi query algorithms into one efficient algorithm.

This paper presents a comprehensive comparative analysis of two optimization algorithms: the widely-adopted Adam optimizer and the recently proposed MuonClip algorithm. Optimization algorithms play a crucial role in deep learning, directly influencing both convergence speed and final model performance. Through systematic comparison of mathematical structures, computational complexity, convergence characteristics, and empirical performance, we analyze the strengths and limitations of each algorithm. Our findings suggest that MuonClip offers superior memory efficiency and stability, while Adam provides finer adaptivity and broader applicability. These insights provide practical guidelines for algorithm selection in different scenarios.

Human being are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solution to these problems. Nature acts as an optimizer for solving the complex problems. In this paper, the algorithms which are discussed imitate the processes running in nature. And due to this these process are named as “Nature Inspired Algorithms”. The algorithms inspired from human body and its working and the algorithms inspired from the working of groups of social agents like ants, bees, and insects are the two classes of solving such Problems. This emerging new era is highly unexplored young for the research. This paper proposes the high scope for the development of new, better and efficient techniques and application in this area.

For Wi-Fi and IoT wireless systems, high-capacity wireless communication technology is being applied using MIMO OFDM. Because of virtu0al subcarriers in the practical MIMO OFDM system, the pilot subcarriers cannot be spaced equally. Thus, it is difficult to obtain a good mean square error (MSE) performance of the channel estimate. This paper proposes applicable methods and the newly discovered locations of pilot subcarriers in four transmitted antennas resulting in a good MSE performance with proposed optimization algorithms.

In this study, a multi-input multi-output (MIMO) semi-active fuzzy control algorithm has been developed for vibration control of a seismically excited building structure. The MIMO fuzzy controller was optimized by evolutionary genetic algorithm. For numerical simulation, a five-story example building structure is used and two MR dampers are employed as semi-active control devices. For comparison purpose, a clipped-optimal control strategy based on acceleration feedback is employed for controlling MR dampers to reduce structural responses due to seismic loads. Numerical simulation results show that the MIMO fuzzy control algorithm can provide superior control performance to the clipped-optimal control algorithm. When the design method proposed in this study is applied, Pareto optimal solutions can be obtained in single optimization run. Therefore, an alternative solution can be easily selected by an engineer.