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heuristic algorithms for RIS-Assisted Wireless Networks: Exploring heuristic-Aided Machine Learning

IEEE WIRELESS COMMUNICATIONS 2024年第4期31卷 106-114页

作者： Zhou, Hao Erol-Kantarci, Melike Liu, Yuanwei Poor, H. Vincent Univ Ottawa Ottawa ON Canada Queen Mary Univ London London England Princeton Univ Princeton NJ USA

Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms: heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQNs). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.

关键词： heuristic algorithms Wireless networks Machine learning algorithms Metaheuristics Signal processing algorithms Greedy algorithms Training

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heuristic algorithms for the Planar Intermodal p-Hub Location: A Possibilistic Clustering Approach 18th

Heuristic Algorithms for the Planar Intermodal p-Hub Locat...

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18th International Conference on Learning and Intelligent Optimization, LION 2024

作者： Basallo-Triana, Mario José Vidal-Holguín, Carlos Julio Bravo-Bastidas, Juan José Basallo-Triana, Yesid Fernando School of Industrial Engineering Universidad del Valle sede Buga Carrera 13 No. 5–21 Guadalajara de Buga Colombia School of Industrial Engineering Universidad del Valle Calle 13 No 100–00 Santiago de Cali Colombia Engineering Department Universidad Autónoma de Occidente Calle 25 No 115–85 Santiago de Cali Colombia

ISBN: (纸本)9783031756221

The design of intermodal hub networks is of paramount importance in logistic operations involving multiple transportation modes like trains and trucks. In this work, we consider two non-linear optimization models for the intermodal p-hub network design problem assuming that hubs can be located anywhere in a two-dimensional space. The first formulation focuses on minimizing congestion in transport activities, while the second formulation aims to maximize hub usage using the concept of entropy. To solve the problem, we propose two heuristic algorithms which are generalizations of well-known possibilistic clustering algorithms. These heuristics are relatively easy to implement and provide a practical way to efficiently solve problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： heuristic algorithms

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Evaluating Meta-heuristic algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound Method

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2024年第4期19卷 31-44页

作者： Tong, Hao Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Birmingham England Honda Res Inst Europe GmbH Offenbach Germany Lingnan Univ Hong Kong Peoples R China

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances.

关键词： heuristic algorithms Metaheuristics Avalanche photodiodes Evolutionary computation Routing Approximation algorithms Vehicle dynamics Time complexity Performance evaluation Benchmark testing Optimization methods

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Path Planning Optimization of the Load Transport Process Using heuristic algorithms

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9940页

作者： Kwiaton, Pawel Cekus, Dawid Skrobek, Dorian Sofer, Michal Poruba, Zdenek Czestochowa Tech Univ Fac Mech Engn Dept Mech & Machine Design Fundamentals Dabrowskiego 73 PL-42201 Czestochowa Poland Jan Dlugosz Univ Czestochowa Fac Sci & Technol Dept Adv Computat Methods Armii Krajowej 13-15 PL-42200 Czestochowa Poland VSB Tech Univ Ostrava Fac Mech Engn Dept Appl Mech 17 Listopadu 15-2127 Ostrava 70833 Czech Republic

The paper presents the process of optimizing the duty cycle of a rotary crane. The minimization of the carried load's trajectory was chosen as the objective function. The research was conducted using the genetic algorithm and the particle swarm algorithm. The influence of particular algorithm parameters on the obtained optimal solution was characterized. For the obtained best case, the inverse kinematics problem was solved, allowing us to determine the control functions of individual crane members. The presented redundant system was solved with the use of an algorithm for temporarily limiting the movement of specific kinematic pairs. On the basis of the obtained results, it was determined which of the algorithms used is more favorable, taking into account the crane's operational safety and lifting capacity.

关键词： heuristic algorithms optimization load transport PSO genetic algorithm

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Multi-objective fog node placement strategy based on heuristic algorithms for smart factories

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WIRELESS NETWORKS 2024年第6期30卷 5407-5424页

作者： Xu, Fulong Yin, Zhenyu Han, Guangjie Li, Yue Zhang, Feiqing Bi, YuanGuo Chinese Acad Sci Shenyang Inst Comp Technol Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Liaoning Key Lab Domest Ind Control Platform Techn Shenyang Peoples R China Hohai Univ Changzhou Peoples R China Northeastern Univ Shenyang Peoples R China Minist Educ Engn Res Ctr Secur Technol Complex Network Syst Shenyang Peoples R China

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 -1089ms over the benchmark algorithm, saving 18.3 -39% in energy consumption. The total latency of IMOA is reduced by 59.8 -68.5%, and the maximum latency is reduced by 48.8 -69.2%. The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption.

关键词： Smart factory Fog computing Node placement heuristic algorithms

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A fuzzy stochastic model incorporating advance sales, discounts, and carbon emission factors with comparative analysis of tuned meta-heuristic algorithms

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Soft Computing 2024年第23期28卷 13167-13203页

作者： Keswani, Mamta Khedlekar, Uttam Kumar Department of Mathematics & Statistics Dr. Harisingh Gour Central University UTD MP Sagar470003 India

In a competitive market, technological advancements opens many opportunities for retailing businesses to increase their profitability through innovative strategies, including discount offers, pre-order programs, and online payment services. However, this surge in competition has led to challenges in optimizing inventory decisions. This study addresses these multifaceted challenges by focusing on key decision variables such as selling price, replenishment schedules, and costs associated with advance sales strategies. The present study bifurcates the inventory replenishment cycle into two distinct sales phases: the two-phase for the advance sales period and the one-phase for the spot sale period. This segmentation enables a more refined examination of sale strategies and customer behaviours, encompassing the flexibility for customers to cancel orders and return products during the return period. We also have incorporated innovative strategies like discount offers, pre-order programs, and online payment services to enhance sales. One of the core contributions of this research lies in modelling demand in a fuzzy-stochastic environment, acknowledging the inherent uncertainties in customer demand influenced by factors such as selling price and advertisement costs. Through theoretical analysis, the proposed study formulates theorems that aid in developing objective functions in a fuzzy stochastic environment. The corresponding profit maximization problem is derived in this environment and employs the weighted sum method to defuzzify the maximization problem. Beyond profit maximization, this research delves into environmental considerations, assessing the impact of carbon emissions and associated taxes on inventory decisions. Subsequently, we solve it by applying three distinct optimization methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO-CO), and the Analytical Method (AM). Then, the proposed model is justified and validated through numerical examples invo

关键词： heuristic algorithms

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heuristic algorithms for Demand Provisioning in Hybrid Single/Multi-band Elastic Optical Networks 8

Heuristic Algorithms for Demand Provisioning in Hybrid Singl...

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8th Optoelectronics Global Conference, OGC 2023

作者： Din, Der-Rong Ncue Department of Csie Changhua City Taiwan

ISBN: (纸本)9798350325324

Since upgrading the single-band EON (SB-EON) to multi-band (MB-EON) is a staged and lengthy process, fibers with SB and MB transmission capacities may coexist for an extend period. This type of network is referred to as a hybrid SB/MB EON. In this article, a family of routing, band, and spectrum allocation algorithms for hybrid SB/MB-EONs is proposed. The bandwidth blocking probability (BBP) of the proposed algorithms is simulated and compared. The proposed heuristic algorithms are also evaluatedon networks with different numbers of fibers with MB transmission. The results show that the three new algorithms outperform the previous proposal method V3 on a hybrid SB/MB- EON with heavier loads. These findings are expected to contribute to the advancement of dynamic resource allocation in SB/MB-EONs and network upgrades. © 2023 IEEE.

关键词： heuristic algorithms

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Applications of Novel heuristic algorithms in Design Optimization of Energy-Efficient Distribution Transformer

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IEEE ACCESS 2023年 11卷 15968-15980页

作者： Hashemi, Mohammad Hassan Kilic, Ulas Dikmen, Selim Ege Univ Elect & Elect Engn Dept TR- 35040 Izmir Turkiye Eltas Transformator AS Res & Dev Dept TR-35800 Izmir Turkiye

Transformers are crucial and expensive assets of power grids. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces the costs for the utility company and consumers. Losses in the transformer generate heat, which can reduce the lifespan of the transformer and require additional cooling. Additionally, reducing losses can help to decrease greenhouse gas emissions associated with the generation of electricity. This study presents an optimization method for transformer design problem using variables that have a great impact on the performance of a transformer. Due to the non-convex nature of the transformer design problems, the empirical methods fail to find the optimal solution and the design process is very tedious and time-consuming. Considering No Free Lunch (NFL) theorem, the design problem is solved using four novel heuristic optimization algorithms, the Firefly Optimization Algorithm (FA), Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimization Algorithm (GWO), and Artificial Gorilla Troops Optimizer Algorithm (GTO) and the results are compared to an already manufactured 1000 kVA eco-friendly distribution transformer using the empirical methods. The outcome of the optimization shows that the suggested method along with the algorithms mentioned leads to a notable decrease in power losses by up to 3.5%, and a reduction in transformer weight by up to 8.3%. This leads to an increase in efficiency, decreased costs for materials, longer lifespan and a reduction in emissions. The developed model is capable of optimally designing oil-immersed distribution transformers with different power ratings and voltage levels.

关键词： Oil insulation Optimization Costs Power transformer insulation Transformer cores heuristic algorithms Finite element analysis Ecodesign Transformers Eco-design transformer energy efficiency optimization power losses transformer design

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Optimization of chemical synthesis with heuristic algorithms

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PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2023年第5期25卷 4323-4331页

作者： Chen, Jialu Xu, Wenjun Zhang, Ruiqin City Univ Hong Kong Dept Phys Hong Kong Peoples R China Beijing Computat Sci Res Ctr Beijing 100193 Peoples R China

Optimizing reaction conditions to improve the yield is fundamental for chemical synthesis and industrial processes. Experiments can only be performed under a small portion of reaction conditions for a system, so a strategy of experimental design is required. Bayesian optimization, a global optimization algorithm, was found to outperform human decision-making in reaction optimization. Similarly, heuristic algorithms also have the potential to solve optimization problems. In this work, we optimize these reaction conditions for Buchwald-Hartwig and Suzuki systems by predicting reaction yields with three heuristic algorithms and three encoding methods. Our results demonstrate that particle swarm optimization with numerical encoding is better than the genetic algorithm or simulated annealing. Moreover, its performance is comparable to Bayesian optimization without the computational costs of descriptors. Particle swarm optimization is simple and easy to perform, and it can be implemented into laboratory practice to promote chemical synthesis.

关键词： heuristic algorithms

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Benchmarking of heuristic algorithms for Energy Router-Based Packetized Energy Management in Smart Homes

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 2721-2732页

作者： Hussain, Hafiz Majid Ahmad, Ashfaq Narayanan, Arun Nardelli, Pedro H. J. Yang, Yongheng Lappeenranta Univ Technol Dept Elect Engn Lappeenranta 53850 Finland Air Univ Dept Elect & Comp Engn Islamabad 44000 Pakistan Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.

关键词： Smart homes Costs Delays Biological system modeling Optimization heuristic algorithms Scheduling Energy Internet (EI) energy router (ER) heuristic algorithms packetized energy management systems (PEMs)

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