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A comparison of heuristic algorithms for custom instruction selection

MICROPROCESSORS AND MICROSYSTEMS 2016年第PartA期45卷 176-186页

作者： Wang, Shanshan Xiao, Chenglong Liu, Wanjun Casseau, Emmanuel Liaoning Tech Univ Fuxin Liaoning Peoples R China Univ Rennes 1 Inria F-35014 Rennes France

Extensible processors with custom function units (CFU) that implement parts of the application code can make good trade-off between performance and flexibility. In general, deciding profitable parts of the application source code that run on CFU involves two crucial steps: subgraph enumeration and subgraph selection. In this paper, we focus on the subgraph selection problem, which has been widely recognized as a computationally difficult problem. We have formally proved that the upper bound of the number of feasible solutions for the subgraph selection problem is 3(n/3), where n is the number of subgraph candidates. We have adapted and compared five popular heuristic algorithms: simulated annealing (SA), tabu search (TS), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO), for the subgraph selection problem with the objective of minimising execution time under non-overlapping constraint and acyclicity constraint. The results show that the standard SA algorithm can produce the best results while taking the least amount of time among the five standard heuristics. In addition, we have introduced an adaptive local optimum searching strategy in ACO and PSO to further improve the quality of results. (C) 2016 Elsevier B.V. All rights reserved.

关键词： heuristic algorithms Data-flow graph Extensible processors Custom instructions Custom function units

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Efficient heuristic algorithms for Single-Vehicle Task Planning With Precedence Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 6274-6283页

作者： Bai, Xiaoshan Cao, Ming Yan, Weisheng Ge, Shuzhi Sam Zhang, Xiaoyu Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Groningen Fac Sci & Engn NL-9747 Groningen Netherlands Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed;in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.

关键词： Task analysis Planning heuristic algorithms Genetic algorithms Optimization Sorting Cybernetics heuristic algorithms lower bound precedence constraints task planning topological sorting

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Applying heuristic algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 318-331页

作者： Chen, Ping-Shun Huang, Wen-Tso Chiang, Tsung-Huan Chen, Gary Yu-Hsin Chung Yuan Christian Univ Dept Ind & Syst Engn Chung Li Dist 320 Taoyuan County Taiwan Minnan Normal Univ Business Sch 36 Qian Zhi St Zhangzhou Fujian Peoples R China Anal & Applicat Business Big Data Key Lab Univ Fu 36 Qian Zhi St Zhangzhou Fujian Peoples R China Natl Kaohsiung Univ Sci Technol Dept Logist Management Kaohsiung 824 Taiwan

To address the inter-hospital hierarchical allocation and scheduling problems, this research used the pooling resource concept to allocate medical staff among hospital branches as well as determine their monthly schedules. This study proposed a two-stage strategy. The first stage proposed three heuristic algorithms-HRA1 (human resource allocation based on the hospital's size), HRA2 (human resource allocation based on the average allocation), and HRA3 (human resource allocation based on the severity of penalties) for medical staff allocation. The second stage used the improved particle swarm optimization (PSO) algorithm to schedule the medical staff within a reasonable time. Based on the numerical results, HRA3 was superior to HRA1 and HRA2. Furthermore, the analysis of two scenarios-varying the sizes of hospital branches (Scenario 1) and varying the total number of medical staff (Scenario 2)-showed that, when the sizes of hospital branches varied (Scenario 1), HRA3 was superior to HRA1 and HRA2 whereas, when the sizes were given (Scenario 2), the lowest number of medical staff possible was approximately 60. The findings of this research will help hospital managers make decisions concerning the allocation and scheduling of medical staff. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： heuristic algorithms Staff allocation Staff scheduling Inter-hospital Particle swarm optimization

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Two heuristic algorithms for the Minimum Initial Marking Problem of Timed Petri Nets

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2013年第2期E96A卷 540-553页

作者： Ochiiwa, Satoru Taoka, Satoshi Yamauchi, Masahiro Watanabe, Toshimasa Hiroshima Univ Grad Sch Engn Higashihiroshima 7398527 Japan Kinki Univ Sch Engn Higashihiroshima 7392116 Japan

A timed Petri net, an extended model of an ordinary Petri net with introduction of discrete time delay in firing activity, is practically useful in performance evaluation of real-time systems and so on. Unfortunately though, it is often too difficult to solve (efficiently) even most basic problems in timed Petri net theory. This motivates us to do research on analyzing complexity of Petri net problems and on designing efficient and/or heuristic algorithms. The minimum initial marking problem of timed Petri nets (TPMIM) is defined as follows: "Given a timed Petri net, a firing count vector X and a nonnegative integer pi, find a minimum initial marking (an initial marking with the minimum total token number) among those initial ones M each of which satisfies that there is a firing scheduling which is legal on M with respect to X and whose completion time is no more than pi, and, if any, find such a firing scheduling." In a production system like factory automation, economical distribution of initial resources, from which a schedule of job-processings is executable, can be formulated as TPMIM. The subject of the paper is to propose two pseudo-polynomial time algorithms TPM and TMDLO for TPMIM, and to evaluate them by means of computer experiment. Each of the two algorithms finds an initial marking and a firing sequence by means of algorithms for MIM (the initial marking problem for non-timed Petri nets), and then converts it to a firing scheduling of a given timed Petri net. It is shown through our computer experiments that TPM has highest capability among our implemented algorithms including TPM and TMDLO.

关键词： timed Petri nets legal firing sequence problems deficient siphons heuristic algorithms performance evaluation

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Neural Tensor Network Training Using Meta-heuristic algorithms for RDF Knowledge Bases Completion

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APPLIED ARTIFICIAL INTELLIGENCE 2019年第7期33卷 656-667页

作者： Abedini, Farhad Keyvanpour, Mohammad Reza Menhaj, Mohammad Bagher Islamic Azad Univ Qazvin Branch Fac Comp & Informat Technol Engn Qazvin Iran Alzahra Univ Dept Comp Engn Vanak Iran Amirkabir Univ Technol Dept Elect Engn Ctr Excellence Control & Robot Tehran Iran

Neural tensor network (NTN) has been recently introduced to complete Resource Description Framework (RDF) knowledge bases, which has been the state-of-the-art in the field so far. An RDF knowledge base includes some facts from the real world shown as RDF "triples." In the previous methods, an objective function has been used for training this type of network, and the network parameters should have been set in a way to minimize the function. For this purpose, a classic nonlinear optimization method has been used. Since many replications are needed in this method to get the minimum amount of the function, in this paper, we suggest to combine meta-heuristic optimization methods to minimize the replications and increase the speed of training consequently. So, this problem will be improved using some meta-heuristic algorithms in this new approach to specify which algorithm will get the best results on NTN and its results will be compared with the results of the former methods finally.

关键词： network parameters Tensor heuristic algorithms Meta-heuristic algorithms Knowledge Bases CX3CL1 wt Allele Optimization methods Resource Description Framework Completion optimization method

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A comparative runtime analysis of heuristic algorithms for satisfiability problems

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ARTIFICIAL INTELLIGENCE 2009年第2期173卷 240-257页

作者： Zhou, Yuren He, Jun Nie, Qing S China Univ Technol Sch Engn & Comp Sci Guangzhou 510640 Peoples R China Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Ceredigion Wales Univ Calif Irvine Dept Math Irvine CA 92697 USA

The satisfiability problem is a basic core NP-complete problem. In recent years, a lot of heuristic algorithms have been developed to solve this problem, and many experiments have evaluated and compared the performance of different heuristic algorithms. However, rigorous theoretical analysis and comparison are rare. This paper analyzes and compares the expected runtime of three basic heuristic algorithms: RandomWalk, (1 + 1) EA, and hybrid algorithm. The runtime analysis of these heuristic algorithms on two 2-SAT instances shows that the expected runtime of these heuristic algorithms can be exponential time or polynomial time. Furthermore, these heuristic algorithms have their own advantages and disadvantages in solving different SAT instances. It also demonstrates that the expected runtime upper bound of RandomWalk on arbitrary k-SAT (k >= 3) is 0((k - 1)(n)), and presents a k-SAT instance that has Theta((k - 1)(n)) expected runtime bound. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Boolean satisfiability heuristic algorithms Random walk (1+1) EA Hybrid algorithm Expected first hitting time Runtime analysis

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Location-based optimal sizing of hybrid renewable energy systems using deterministic and heuristic algorithms

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第11期45卷 16155-16175页

作者： Demolli, Halil Dokuz, Ahmet Sakir Ecemis, Alper Gokcek, Murat Univ Prishtina Fac Mech Engn Prishtina Kosovo Nigde Omer Halisdemir Univ Dept Comp Engn TR-51240 Nigde Turkey Nigde Omer Halisdemir Univ Dept Mech Engn Nigde Turkey

The application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost-effective system based on given load demand and techno-economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost-efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems.

关键词： heuristic algorithms hybrid renewable energy systems LCOE LPSP photovoltaic system wind energy

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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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Novel Architecture and heuristic algorithms for Software-Defined Wireless Sensor Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第6期28卷 2809-2822页

作者： Hawbani, Ammar Wang, Xingfu Zhao, Liang Al-Dubai, Ahmed Min, Geyong Busaileh, Omar Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting-time and the duplicate packets. To this end and beyond the state-of-the-art, this work proposes an SDN-based architecture, namely MINI-SDN, that separates the control and data planes. Moreover, based on MINI-SDN, we propose MINI-FLOW, a communication protocol that orchestrates the computation of flows and data routing between the two planes. MINI-FLOW supports uplink, downlink and intra-link flows. Uplink flows are computed based on a heuristic function that combines four values, the hops to the sink, the Received Signal Strength (RSS), the direction towards the sink, and the remaining energy. As for the downlink flows, two heuristic algorithms are proposed, Optimized Reverse Downlink (ORD) and Location-based Downlink(LD). ORD employs the reverse direction of the uplink while LD instantiates the flows based on a heuristic function that combines three values, the distance to the end node, the remaining energy and RSS value. Intra-link flows employ a combination of uplink/downlink flows. The experimental results show that the proposed architectureand communication protocol perform and scale well with both network size and density, considering the joint problem of routing and load balancing.

关键词： Wireless sensor networks Computer architecture Routing Protocols heuristic algorithms Uplink Downlink Wireless sensor networks heuristic routing MINI-FLOW MINI-SDN

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The Marking Construction Problem of Petri Nets and Its heuristic algorithms

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2011年第9期E94A卷 1833-1841页

作者： Taoka, Satoshi Watanabe, Toshimasa Hiroshima Univ Grad Sch Engn Higashihiroshima 7398527 Japan

The marking construction problem (MCP) of Petri nets is defined as follows: "Given a Petri net N, an initial marking M-i and a target marking M-t, construct a marking that is closest to Mt among those which can be reached from M-i by firing transitions." MCP includes the well-known marking reachability problem of Petri nets. MCP is known to be NP-hard, and we propose two schemas of heuristic algorithms: (i) not using any algorithm for the maximum legal firing sequence problem (MAX LFS) or (ii) using an algorithm for MAX LFS. Moreover, this paper proposes four pseudo-polynomial time algorithms: MCG and MCA for (i), and MCHFk and MC_feideq_a for (ii), where MCA (MC_feideq_a, respectively) is an improved version of MCG (MCHFk). Their performance is evaluated through results of computing experiment.

关键词： Petri nets marking construction problem reachability problem heuristic algorithms computer experiments

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