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Joint optimization of space allocation and yard crane deployment in container terminal under uncertain demand

COMPUTERS & INDUSTRIAL ENGINEERING 2022年第PartA期172卷

作者： Yang, Xurui Hu, Hongtao Jin, Jiangang Luo, Nan Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China

The fluctuation of container transportation volume caused by trade policy, epidemic disease and other factors has a significant impact on the terminal yard management. Especially, the decision of yard space problem and yard crane deployment problem is depending on the arriving container volume. Due to the limited space and mechanical resources of the terminal yard, the uncertain container demand will lead to the waste or shortage of yard resources. In order to improve the efficiency of yard management, this paper studies the integrated optimization of yard resources considering the impact of uncertain container demand, and a stochastic optimization model is established to improve the robustness of the daily yard management. Due to the complexity of the model, a harmony search algorithm based on two-stage decomposition strategy is designed to solve the proposed model. To accelerate the solving speed, a scoring method is developed to generate the initial space allocation plan, and a Tabu list is added to improve the solution quality. The numerical experiments are conducted to validate effectiveness and efficiency of the method.

关键词： Container terminal Space allocation Yard crane deployment Uncertainty harmony search algorithm

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A metaheuristic-based optimum tuning approach for tuned liquid dampers for structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第3期31卷 e1907-e1907页

作者： Ocak, Ayla Bekdas, Gebrail Nigdeli, Sinan Melih Istanbul Univ Cerrahpasa Dept Civil Engn Istanbul Turkey

This study focuses on the optimization of tuned liquid dampers (TLDs) by using metaheuristic methods using a natural phenomenon as inspiration. Unlike tuned mass dampers (TMDs), TLDs consist of two parts of bodies that contain the passive liquid that is stationary and the active part of the liquid that involves in sloshing. To optimize the amount of optimum active and passive liquids by finding the best dimensional properties of the liquid tank, a two-degree-of-freedom (DOF) model was used as TLD in the dynamic analysis used in the optimization that aims to reduce the maximum displacement of the structure under earthquake excitations. For this purpose, water, oil, and glycerin liquids were selected for TLD. TLD parameters were optimized with the help of Jaya algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, and teaching-learning-based optimization (TLBO). Total acceleration and maximum displacement were evaluated after optimization. The performance of TLD under earthquake excitations has been also compared with TMDs. According to the results, the optimized TLD has a better performance than TMD, and Jaya algorithm has a slightly better performance than the other algorithms.

关键词： flower pollination algorithm harmony search algorithm Jaya algorithm optimization teaching-learning-based optimization tuned liquid damper (TLD)

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Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic algorithms to Generate Energy from Existing Water Supply Networks

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ENERGIES 2022年第14期15卷 5166页

作者： Karakatsanis, Diamantis Theodossiou, Nicolaos Aristotle Univ Thessaloniki Dept Civil Engn Div Hydraul & Environm Engn Thessaloniki 54124 Greece

In this paper, the possibility of installing small hydraulic turbines in existing water-supply networks, which exploit the daily pressure fluctuations in order to produce energy, is examined. For this purpose, a network of five pressure sensors is developed, which is connected to an artificial intelligence system in order to predict the daily pressure values of all nodes of the network. The sensors are placed at the critical nodes of the network. The locations of the critical nodes are implemented by applying graph theory algorithms to the water distribution network. EPANET software is used to generate the artificial intelligence training data with an appropriate external call from a Python script. Then, an improvement model is implemented using the harmony search algorithm in order to calculate the daily pressure program, which can be allocated to the turbines and, consequently, the maximum energy production. The proposed methodology is applied to a benchmark water supply network and the results are presented.

关键词： WDNs harmony search algorithm machine learning graph theory micro hydro generator wireless sensor network

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An Improved harmony search algorithm Based on Teaching-Learning Strategy

An Improved Harmony Search Algorithm Based on Teaching-Learn...

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32nd Chinese Control Conference (CCC)

作者： Tuo Shouheng Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China

ISBN: (纸本)9789881563835

harmony search (HS) algorithm inspired by the music improvisation process is a swarm intelligent algorithm. In this paper, an improved global harmony search algorithm which is based on Teaching-Learning-Based-Optimization (TLBO) algorithm is proposed to solve the complex high-dimensional optimization problems. In the proposed algorithm, harmony memory consideration, teaching-learning strategy, local pitch adjusting and random operation method are adopted to improve the search ability of HS. The parameters are adjusted dynamically in terms of the process of iterations. Finally, the proposed algorithm is tested and compared with three other state-of-the-art HS optimization algorithms. The experimental results on 7 high-dimensional benchmark functions show that the HSTL method has strong convergence, and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

关键词： harmony search algorithm Teaching-Learning Based Optimization complex high-dimensional optimization problems

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Proposing and Evaluation of SC Techniques for Variable Speed High-Power Operation of SEIG

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IEEE ACCESS 2020年 8卷 20666-20675页

作者： Ahmed, Marwa M. Hassanein, Wael S. Enany, Mohamed A. King Abdulaziz Univ Fac Engn Elect Engn Dept Jeddah 80204 Saudi Arabia King Abdulaziz Univ Fac Engn Ind Engn Dept Jeddah 80204 Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig 44519 Egypt

This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR) for achieving constant rated stator current. WDSEIG would produce a variable speed high power without overheating under variable wind speed and connected load. The analytical approach for the proposed methodology has been implemented to predict the optimal operating firing angle of FC-TCR for full load stator current achievement within the allowed operating range of load and prime mover speed. Also, Soft Computing (SC) techniques have been implemented based on harmony search algorithm (HSA), Flower Pollination algorithm (FPA), and Moth-Flame Optimization (MFO) algorithm to achieve the proposed methodology. A comparison between different SC techniques, analytical approach and experimental work are given and evaluated to verify SC techniques accuracy. This evaluation study can be useful in specifying the appropriateness of the SC techniques for High Power Transfer Capability for a SEIG.

关键词： Self-excited induction generator fixed capacitor-thyristor controlled reactor variable speed high power transfer capability moth-flame optimization algorithm flower pollination algorithm harmony search algorithm

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A hybrid technique for simultaneous network reconfiguration and optimal placement of distributed generation resources

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SOFT COMPUTING 2020年第15期24卷 11315-11336页

作者： Quadri, Imran Ahmad Bhowmick, S. Delhi Technol Univ Dept Elect Engn Delhi India

A new meta-heuristic method, comprehensive teaching learning harmony search optimization algorithm (CTLHSO), is developed in this paper for the simultaneous reconfiguration and optimal allocation of distributed generation resources in radial distribution systems. The proposed method is a hybridization of the teaching-learning-based optimization (TLBO) and the harmony search (HS) algorithms. Primarily, eleven mathematical benchmark functions are used to test the performance of the CTLHSO algorithm. The results are then compared with that of global best artificial bee colony (G-ABC), particle swarm artificial bee colony (PS-ABC), TLBO and improved TLBO (I-TLBO) with identical parameters and initial conditions. The results show that the CTLHSO performance is better than the G-ABC, PS-ABC, TLBO and I-TLBO. Subsequently, CTLHSO is implemented on the IEEE 33-bus and 69-bus radial distribution systems for network reconfiguration and optimal placement of distributed generation resources to minimize the power losses and improve the voltage profiles. Five case studies at three different load levels are carried out. The results obtained are found to be better than those obtained with HS algorithm, genetic algorithm, refined genetic algorithm and fireworks algorithm.

关键词： Distributed generation harmony search algorithm Teaching-learning-based optimization Network reconfiguration

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Combining an arti ficial intelligence algorithm and a novel vehicle for sustainable e-waste collection

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 730卷 138726-138726页

作者： Nowakowski, Piotr Szwarc, Krzysztof Boryczka, Urszula Silesian Tech Univ Fac Transport Ul Krasinskiego 8 PL-40019 Katowice Poland Univ Silesia Katowice Inst Comp Sci Ul Bedzinska 39 PL-41200 Sosnowiec Poland

Mobile collection of waste electrical and electronic equipment is a collection method that is convenient for residents and companies. New opportunities to use mobile apps and internet applications facilitate the ordering of waste pickups from households and preparation of a collection plan for a waste collection company. It improves the secondary raw materials collection in a circular economy approach after recycling waste equipment. This study presents a combined methodology for improving the efficiency of e-waste collection. An online e-waste collection supporting system uses a harmony search algorithm for route optimization of waste collection vehicles. The results of the optimization are better compared to other artificial intelligence algorithms presented in the literature and the number of visited collection points is higher from 1.2%-6.6% depending on the compared algorithm. To increase the efficiency of waste loading and packing, a novel collection vehicle body construction is presented. The design includes the convenient loading of waste from both sides of the vehicle and the rear side being equipped with a hydraulic lift. The proposed vehicle model can be used for e-waste collection in places with limited parking spaces or where the parking time is limited, such as in densely populated city centers. The waste equipment packing efficiency increases and eliminates the necessity of including a container loading problem in the algorithm and allows increasing waste equipment number loaded in a collection vehicle. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Mobile collection of e-waste Waste electrical and electronic equipment E-waste Waste collection vehicle Circular economy harmony search algorithm

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Designing a New Radial Basis Function Neural Network by harmony search for Diabetes Diagnosis

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OPTICAL MEMORY AND NEURAL NETWORKS 2019年第4期28卷 321-331页

作者： Giveki, Davar Rastegar, Homayoun Malayer Univ Dept Comp Engn POB 65719-95863 Malayer Iran Afarinesh Inst Higher Educ Dept Comp Engn Boroujerd Iran

Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks. So, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. Hence, an improved learning algorithm for center adjustment of RBFNNs using harmony search (HS) algorithm has been proposed. The proposed RBFNN is used for diabetes recognition task. In order to increase the recognition accuracy as well as to reduce the dimensionality of feature vectors, Rough Set Theory (RST) has been applied on Pima Indians Diabetes. Comprehensive experiments have been conducted on Proben1 dataset in order to evaluate the efficiency and accuracy of the proposed RBFNN. The experimental results show that the proposed method can achieve higher performance compared to other state-of-the-art in the field.

关键词： Radial Basis Function Neural Network harmony search algorithm Diabetes diagnosis Rough Set Theory

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Implementation of Efficient Intra- and Inter-Zone Routing for Extending Network Consistency in Wireless Sensor Networks

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第8期29卷

作者： Prasanth, A. Pavalarajan, S. PSNA Coll Engn & Technol Dept Elect & Commun Engn Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept Informat Technol Dindigul Tamil Nadu India

Wireless sensor network (WSN) consists of a large amount of limited battery-powered sensor nodes. In general, energy consumption will be a significant concern for WSN owing to irreplaceable battery constraints of sensor nodes. The zone formation approach could be an adequate data aggregation technique which efficiently minimizes the energy consumption by categorizing sensor nodes into zones. However, the main constraints like zone head (ZH) selection, frequent change of ZH, and multi-hop communication from ZH to the sink have a direct impact on the network consistency of WSN. In this paper, a novel efficient intra- and inter-zone routing scheme has been proposed in order to prolong the network consistency of WSN. In the proposed scheme, the hybrid algorithm is established in which harmony search algorithm incorporates with modified moth flame optimization algorithm. This hybrid algorithm provides the appropriate ZH selection for intra-zone routing that reduces the frequent change of ZH in the network. Furthermore, the path balancing in inter-zone routing is acquired through multicriteria-based optimal path routing algorithm. The performance results confirm that the proposed scheme enhances the network consistency compared with an existing scheme.

关键词： Energy consumption network consistency network lifetime harmony search algorithm wireless sensor network

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Analysis of Subway Passenger Flow for a Smarter City: Knowledge Extraction From Seoul Metro's 'Untraceable' Big Data

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IEEE ACCESS 2020年 8卷 69296-69310页

作者： Shin, Hyunkyung Gachon Univ Dept Financial Math Seongnam 13120 South Korea

Timely and efficient analysis of big data collected from various gateways installed in a smart city is an intractable problem and requires immediate priority. Given the stochastic and massive nature of big data, the existing literature often relies on artificial intelligence techniques based on information theory. As a new approach, this paper presents a knowledge extraction method based on an analysis of Seoul Metro's 'untraceable' ridership big data. Without identification information, the untraceable ridership data only shows the hourly accumulation of station entry and exit information. To reconstruct the missing information in the data set, this study proposes a fluid dynamics model and adopts a heuristic genetic algorithm based on optimization theory as the problem solver. The result of our model presents the distribution of the elapsed time defined on an hourly basis taken until a passenger returns to the station they departed from. To validate our model, we acquired subway ridership data with passengers' identification with permission from Seoul Metro. This paper presents two novel aspects of subway ridership, namely the dependency on departure time and the discrepancy between weekend and weekday traffic. Our analytical approach contributes to solving the problem of extracting hidden knowledge from big collection of data missing critical information, e.g., constantly and autonomously gathered data fragments from numerous gateways in smart cities.

关键词： Public transportation Big Data Smart cities Data mining Data models Genetic algorithms Inverse problem genetic algorithm (GA) optimization wave decomposition harmony search algorithm mass conservation law data mining outdoor duration time Seoul metro subway ridership

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