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Joint caching and sleeping optimisation for D2D-aided ultra-dense network

IET COMMUNICATIONS 2020年第1期14卷 1-10页

作者： Li Pei Gao Shen Hu Yaoyue Pan Zhiwen You Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China Purple Mt Labs Nanjing Peoples R China Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Zhejiang Peoples R China

Device-to-device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end-to-end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy-delay tradeoff for D2D-aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub-problem and collaborative caching sub-problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay-aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush-Kuhn-Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.

关键词： telecommunication power management radio networks telecommunication traffic iterative methods cellular radio mobile handsets radio transmitters stochastic processes minimisation derived local optimal small cell caching scheme caching capacity end-to-end delay joint optimisation problem D2D transmitter cell BS heterogeneous caching scheme cost minimisation problem delay-aware sleeping strategy suboptimal caching ratios combinatorial optimisation problem device-to-device communication ultradense small cell networks local optimal sleeping ratio scheme sleeping optimisation subproblem D2D-aided ultradense network D2D communication system energy consumption collaborative caching subproblem Karush-Kuhn-Tucker conditions stochastic traffic model

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An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 622-635页

作者： Choong, Shin Siang Wong, Li-Pei Lim, Chee Peng Univ Sains Malaysia Sch Comp Sci George Town Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic Australia

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimisation of mathematical test functions with a unique neighbourhood search mechanism. This neighbourhood search mechanism could not be directly applied to combinatorial discrete optimisation problems. In order to tackle combinatorial discrete optimisation problems, the employed and onlooker bees need to be equipped with problem-specific perturbative heuristics. However, a large variety of problem-specific heuristics are available, and it is not an easy task to select an appropriate heuristic for a specific problem. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The Lin-Kemighan (LK) local search strategy is integrated to improve the performance of the proposed model. To demonstrate the effectiveness of the proposed model, 64 Traveling Salesman problem (TSP) instances available in TSPLIB are evaluated. On average, the proposed model solves the 64 instances to 0.055% from the known optimum within approximately 2.7 min. A performance comparison with other state-of-the-art algorithms further indicates the effectiveness of the proposed model.

关键词： Hyper-heuristic Metaheuristic Bee algorithm combinatorial optimisation problem Neighbourhood search Lin-Kernighan

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An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第1期57卷 143-165页

作者： Yalcin, Altan Koberstein, Achim Schocke, Kai-Oliver European Univ Viadrina Dept Business Informat Frankfurt Germany Frankfurt Univ Appl Sci Dept Logist Frankfurt Germany

Puzzle-based storage systems consist of densely stored unit loads on a square grid. The problem addressed in this paper is to retrieve a stored unit load from a puzzle-based storage using the minimum number of item moves. While previous research contributed optimal algorithms for only up to two empty locations (escorts), our approach solves configurations where multiple empty locations are arbitrarily positioned in the grid. The problem is formulated as a state space problem and solved to optimality using an exact search algorithm. To reduce the search space, we derive bounds on the number of eligible empty locations and develop several search-guiding estimate functions. Furthermore, we present a heuristic variant of the search algorithm to solve larger problem instances. We evaluate both solution algorithms on a large set of problem instances. Our computational results show that the algorithms clearly outperform existing approaches where they are applicate and solve more general configurations, which could not be solved to optimality before. The heuristic variant efficiently yields high-quality solutions for significantly larger instances of practically relevant size.

关键词： puzzle-based storage compact storage systems automated warehouse combinatorial optimisation problem A* algorithm

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Identifickion of unidentified equality constraints for integer programming problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期260卷 460-467页

作者： Moeini, Asghar Flinders Univ S Australia Sch Comp Sci Engn & Math Tonsley Pk SA 5042 Australia

Characterising the smallest dimension polytope containing all integer solutions of an integer programming problem can be a very challenging task. Frequently, this task is facilitated by identifying linear equality constraints that all integer solutions must satisfy. Typically, some of these constraints are readily available but others need to be discovered by more technical means. This paper develops a method to assist modellers to obtain such equality constraints. Note that the set of new equality constraints is not unique, and the proposed method generates a set of these new equality constraints for a sufficiently large dimension of the underlying problem. These generated constraints may be of a form that is easily extended for general case of the underlying problem, or they may be in a more complicated form where a generalisable pattern is difficult to identify. For the latter case, a new mixed-integer program is developed to detect a pattern-recognisable constraints. Furthermore, this mixed-integer program allows modellers to check if there is a new constraint satisfying specific criteria, such as only permitting coefficients to be 1, 0, and -1, or placing a limit on the number of non-zero coefficients. In order to illustrate the proposed method, a set of new equality constraints to supplement a previously published "Base polytope" are derived. Subsequently, exploiting these results, some techniques are proposed to tighten integer programming problems. Finally, relaxations of widely used TSP formulations are compared against one another and strengthened with help of the newly discovered equality constraints, (C) 2017 Elsevier B.V. All rights reserved.

关键词： Integer programming Convex hull problem combinatorial optimisation problem Extended formulations Traveling salesman problem

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A greedy random adaptive search procedure for the weighted maximal planar graph problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2003年第4期45卷 635-651页

作者： Osman, IH Al-Ayoubi, B Barake, M Amer Univ Beirut Sch Business Ctr Adv Math Sci Beirut Lebanon Lebanese Univ Fac Sci 1 Dept Appl Math Beirut Lebanon

The weighted maximal planar graph (WMPG) problem seeks to find a subgraph from a given weighted complete graph such that the subgraph is planar-it can be embedded on the plane without any arcs intersecting. The subgraph is maximal-no additional arc can be added to the subgraph without destroying its planarity and it also has the maximal sum of arc weights. In this paper, the main objective is to develop, implement and empirically analyse a new greedy random adaptive search procedure (GRASP) to solve the WMPG problem. A dynamic strategy to update the restricted candidate list is proposed. An efficient data structure is developed for the Green&Al-Hakim (GH) construction heuristic. The data structure reduces the GH complexity from O(n(3)) to O(n(2)). The GH heuristic with the data structure is then integrated with advanced moves neighbourhood to develop an efficient GRASP implementation. Further, we investigate the behaviour of GRASP parameters in relation to the problem's characteristics. Finally, the developed algorithms are compared with the best-known procedures in the literature on a set of 100 test instances of sizes varying from 20 to 100 nodes. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： combinatorial optimisation problem data structures facility layout graph theory heuristics weighted maximal planar graph greedy random adaptive search procedure meta-heuristics

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Green-oriented user-satisfaction aware WiFi offloading in HetNets

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IET COMMUNICATIONS 2018年第5期12卷 501-508页

作者： Xu, Jiao Wu, Shaohua Xu, Luyao Zhang, Ning Zhang, Qinyu Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Univ Waterloo Dept Elect Comp Engn Waterloo ON Canada

To cope with the tremendous growth of data traffic and obtain a given communication service with minimal energy use, traffic offloading and energy efficiency (EE) improving are two important issues to address for green cellular networks. The authors investigate downlink WiFi offloading in a heterogeneous network consisting of one long term evolution eNodeB (eNB) and multiple overlaid WiFi access points to maximise the user satisfaction of the whole system. In addition, a designed resource reallocation scheme after offloading is jointly considered to improve the EE of the eNB. In the offloading model, two constraints are considered to guarantee the rate promotion of the offloaded users and less impact on WiFi networks. Moreover, the authors transform the model into a combinatorial optimisation problem and adopt the best response (BR) algorithm based on game-theoretic approach to obtain the optimal offloading user set. Numerical results show that the proposed WiFi-offloading model can significantly improve the aggregate user satisfaction as well as EE of the eNB. Also, the BR algorithm can converge to the optimal solution same as the exhaustive search algorithm through several iterations.

关键词： green computing cellular radio wireless LAN telecommunication traffic combinatorial mathematics game theory optimisation search problems energy conservation telecommunication power management green-oriented user-satisfaction aware WiFi offloading HetNet data traffic communication service minimal energy use traffic offloading energy efficiency green cellular networks downlink WiFi offloading heterogeneous network LTE eNodeB eNB multiple overlaid WiFi access points user satisfaction maximisation rate promotion WiFi networks combinatorial optimisation problem best response algorithm BR algorithm game-theoretic approach optimal offloading user set user satisfaction aggregation exhaustive search algorithm

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Accelerating floating-point to fixed-point data type conversion with evolutionary algorithms

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ELECTRONICS LETTERS 2015年第3期51卷 244-246页

作者： Rosa, L. S. Toledo, C. F. M. Bonato, V. Univ Sao Paulo BR-09500900 Sao Paulo Brazil

The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tuning the right bit length is known as a time-consuming task, since it is a combinatorial optimisation problem guided by the accumulative arithmetic computation error. A novel evolutionary approach to accelerate the process of converting algorithms from the floating-point to fixed-point format is presented. Results are demonstrated by converting three computing-intensive algorithms from the mobile robotic scenario, where data error accumulated during execution is influenced by external factors, such as sensor noise and navigation environment characteristics. The proposed evolutionary algorithm accelerated the conversion process by up to 2.5 x against the state-of-the-art methods, allowing even further bit-length optimisations.

关键词： evolutionary computation floating point arithmetic mobile robots accumulative arithmetic computation error combinatorial optimisation problem data type representation evolutionary algorithms floating-point to fixed-point data type conversion maximum clock frequency mobile robotic scenario power consumption

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Bee colony optimisation algorithm with big valley landscape exploitation for job shop scheduling problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第2期2卷 85-99页

作者： Wong, Li-Pei Puan, Chi Yung Low, Malcolm Yoke Hean Wong, Yi Wen Chong, Chin Soon Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Sains Malaysia Sch Comp Sci Usm 11800 Pulau Pinang Malaysia Singapore Inst Mfg Technol Singapore 638075 Singapore

Job shop scheduling problem (JSSP) is an NP-hard problem that is closely related to scheduling activities in manufacturing industry. This paper presents an improved bee colony optimisation algorithm with Big Valley landscape exploitation (BCBV) as a biologically inspired algorithm to solve the JSSP problem. The BCBV algorithm mimics the bee foraging behaviour where information of newly discovered food source is communicated via waggle dances. In the algorithm, the dances are treated as clusters of solutions to the JSSP. These clusters of solutions are distributed as a Big Valley landscape structure. Via a dance accumulation strategy as well as an effective search in multiple clusters in the entire landscape, the proposed algorithm is able to generate relatively good solutions for the JSSP. Experimental results comparing our proposed algorithm with the shifting bottleneck heuristic (SBP), the tabu search algorithm (TS) and the parameter-free genetic algorithm (PfGA) on the Taillard JSSP benchmark show that it is comparable to these approaches.

关键词： job shop scheduling problem JSSP bee colony optimisation BCO Big Valley combinatorial optimisation problem

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A Survey of University Course Timetabling problem: Perspectives, Trends and Opportunities

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IEEE ACCESS 2021年 9卷 106515-106529页

作者： Chen, Mei Ching Sze, San Nah Goh, Say Leng Sabar, Nasser R. Kendall, Graham Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Sarawak 94300 Malaysia Univ Malaysia Sabah Kampus Antarabangsa Labuan Fac Comp & Informat Optimisat Res Grp Labuan 87000 Malaysia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Univ Nottingham Malaysia Campus Sch Comp Sci Semenyih 43500 Selangor Malaysia

The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies.

关键词： Optimization Color Heuristic algorithms Education Benchmark testing Market research Licenses combinatorial optimisation problem course timetabling problem optimisation

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Analysis of metaheuristic optimisation techniques for simulated matrix production systems

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2024年第1期18卷 159-168页

作者： Benfer, Martin Heyer, Valentin Bruetzel, Oliver Liebrecht, Christoph Peukert, Sina Lanza, Gisela Karlsruhe Inst Technol KIT Wbk Inst Prod Sci Kaiserstr 12 D-76131 Karlsruhe Germany Siemens AG Siemens Digital Ind Ostl Rheinbruckenstr 50 D-76187 Karlsruhe Germany

Increasing demand for individualised products has led to the concept of mass customisation, combining high product variety with production efficiency coming along with mass production. Companies are moving to matrix production systems with complex product flows for mass customisation. One challenge in such systems is the determination of optimal system configurations to fulfil future demands while minimising production costs. An approach to determine the ideal configuration is to use metaheuristics like genetic algorithms or simulated annealing to optimise simulation models. However, it is unclear which methods are ideally suited to finding the best solutions. This contribution compares the performance of genetic algorithms and simulated annealing when optimising the configuration of a company-specific matrix production system using discrete event simulation. The methods are evaluated using different objective functions. For the genetic algorithm, different observation strategies are also tested. Overall, the simulated annealing approach delivers better results with shorter solution times. The contributing factors leading to the different results are discussed, and areas for future research are pointed out.

关键词： Matrix production system combinatorial optimisation problem Genetic algorithm Simulated annealing

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