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Design of Food Exchange List for Obesity using Modified local search 13

Design of Food Exchange List for Obesity using Modified Loca...

13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Dholvitayakhun, Achara Kluabwang, Jukkrit Rajamangala Univ Technol Lanna Tak Fac Sci & Agr Technol Muang 63000 Tak Thailand Rajamangala Univ Technol Lanna Tak Fac Engn Dept Elect Engn Muang 63000 Tak Thailand

ISBN: (纸本)9781467397490

This article presents an application of modified local search (MLS) to design meal's plan for obestity by food exchange list. Both computational experiment and implementation are elaborated. Three obesity cases have been tested with 10 independent trials. The minimum searching time of case I, II, and III are 0.1784, 0.0384 and 0.0784 seconds, respectively. An implementation has selected an obtained result from case II with total energy 1,925 kcal per day, carbohydrate 289 g, protein 93 g and lipid 42 g. After preparation, three main courses, breakfast, lunch and dinner, are macaroni with shrimp soup, baked rice with pine apple and a set of rice with fried mixed vegetables and sour soup, respectively.

关键词： local search algorithm food exchange list ideal body weight

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Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice

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BMC BIOINFORMATICS 2013年第2-sup期14卷 1-13页

作者： Rashid, Mahmood A. Newton, M. A. Hakim Hoque, Md Tamjidul Shatabda, Swakkhar Pham, Duc Nghia Sattar, Abdul Griffith Univ Int Integrated & Intelligent Syst Nathan Qld 4111 Australia Univ New Orleans New Orleans LA 70148 USA

Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy. Results: In order to build H-cores, we present a new Spiral search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core centre. We applied random walks to break premature H-cores and thus to avoid early convergence. We also used a novel relay-restart technique to handle stagnation. Conclusions: We have tested our algorithms on a set of benchmark protein sequences. The experimental results show that our spiral search algorithm outperforms the state-of-the-art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.

关键词： local search Free Energy Hydrophobic Amino Acid local search algorithm Relative Improvement

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Hyper-heuristic local search for combinatorial optimisation problems

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KNOWLEDGE-BASED SYSTEMS 2020年 205卷 106264-106264页

作者： Turky, Ayad Sabar, Nasser R. Dunstall, Simon Song, Andy RMIT Univ Comp Sci & Informat Technol Melbourne Vic 3000 Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia CSIRO Data 61 Docklands Australia

local search algorithms have been successfully used for many combinatorial optimisation problems. The choice of the most suitable local search algorithm is, however, a challenging task as their performance is highly dependent on the problem characteristic. In addition, most of these algorithms require users to select appropriate internal neighbourhood structures to obtain desirable performance. No single local search algorithm can consistently perform well with a fixed setting, for different types of problems or even different instances of the same problem. To address this issue, we propose a hyper-heuristic framework which incorporates multiple local search algorithms and a pool of neighbourhood structures. This framework is novel in three respects. Firstly, a two-stage hyper-heuristic structure is designed to control the selection of a local search algorithm and its internal operators. Secondly, we propose an adaptive ranking mechanism to choose the most appropriate neighbourhood structures for the current local search algorithm. The proposed mechanism uses the entropy to evaluates the contribution of the local search in terms of quality and diversity. It adaptively adjusts the pool of candidate neighbourhood structures. Thirdly, we use a population of solutions within the proposed framework to effectively navigate different areas in the solutions search space and share solutions with local search algorithms. To ensure different solutions is allocated in different regions of the search space, we propose a distance-based strategy for population updating process that allowing solutions to share local search algorithms. We have evaluated the performance of the proposed framework using two challenging optimisation problems: Multi-Capacity Bin Packing benchmark instances and Google Machine Reassignment benchmark instances. The results show the effectiveness of the proposed framework, which outperformed state-of-the-art algorithms on several problem instances. (C) 2020

关键词： Hyper-heuristic local search algorithm Google machine reassignment Multi-capacity bin packing

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A local search method based on edge age strategy for minimum vertex cover problem in massive graphs

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115185-115185页

作者： Quan, Changsheng Guo, Ping Chongqing Univ Coll Comp Sci 174 Shazhengjie Chongqing 400044 Peoples R China Chongqing Key Lab Software Theory & Technol 174 Shazhengjie Chongqing 400044 Peoples R China

Minimum vertex cover problem (MVC) is a classic combinatorial optimization problem, which has many critical real-life applications in scheduling, VLSI design, artificial intelligence, and network security. For MVC, researchers have proposed many heuristic algorithms, especially local search algorithms. And recently, researchers have increased their interest in solving large real-world graphs which require algorithms with faster searching performance. In this work, we propose a new edge weighting method called EABMS. EABMS has a time complexity of O(1). Based on EABMS, we propose our MVC solver framework called EAVC in solving MVC for massive graphs. We conducted experiments and compared the results of EAVC solvers with state of the art solvers. The results show that EABMS is effective in weighing edges for large sparse graphs and EAVC solvers outperform state of the art solvers.

关键词： Minimum vertex cover local search algorithm Edge weighting Combinatorial optimization Heuristic algorithm

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An Effective Biogeography-Based Optimization algorithm for Flow Shop Scheduling with Intermediate Buffers

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Chinese Journal of Electronics 2018年第6期27卷 1141-1150页

作者： LIU Shufen WANG Pengfei YAO Zhilin College of Computer Science and Technology Jilin University

This paper proposes an Effective biogeography-based optimization(EBBO) algorithm for solving the flow shop scheduling problem with intermediate buffers to minimize the Total flow time(TFT). Discrete job permutations are used to represent individuals in the EBBO so the discrete problem can be solved directly. The NEH heuristic and NEH-WPT heuristic are used for population initialization to guarantee the diversity of the solution. Migration and mutation rates are improved to accelerate the search process. An improved migration operation using a two-points method and mutation operation using inverse rules are developed to prevent illegal solutions. A new local search algorithm is proposed for embedding into the EBBO algorithm to enhance local search *** simulations and comparisons demonstrated the superiority of the proposed EBBO algorithm in solving the flow shop scheduling problem with intermediate buffers with the TFT criterion.

关键词： Flow shop scheduling Intermediate buffer Total flow time(TFT) Biogeography-based optimization algorithm local search algorithm

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Rules-based heuristic approach for the U-shaped assembly line balancing problem

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APPLIED MATHEMATICAL MODELLING 2017年 48卷 423-439页

作者： Li, Ming Tang, Qiuhua Zheng, Qiaoxian Xia, Xuhui Floudas, C. A. Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Coll Mech & Automat Wuhan 430081 Hubei Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan 430062 Hubei Peoples R China Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA

The type-2 U-shaped assembly line balancing problem is important for many just-in-time manufactures, but an efficient algorithm is not available at present. Thus, in this study, a novel heuristic approach based on multiple rules and an integer programming model is proposed to address this problem. In the proposed approach, three rules are systematically grouped together, i.e., task selection, task assignment, and task exchange rules. The sufficient conditions for implementing the exchange rules are proposed and proved. Thirteen small or medium scale benchmark issues comprising 63 instances were solved, where the computational results demonstrate the efficiency and effectiveness of the proposed method compared with integer programming. The computational results obtained for 18 examples comprising 121 instances demonstrate that the task exchange rules significantly improve the computational accuracy compared with the traditional heuristic. Finally, 30 new standard instances produced by a systematic data generation process were also solved effectively by the proposed approach. The proposed heuristic approach with multiple rules can provide a theoretical basis for other local search algorithms, especially for addressing issues such as the U-Shaped assembly line balancing problem. (C) 2017 Published by Elsevier Inc.

关键词： Heuristic approach local search algorithm Task exchange rule U-shaped assembly line balancing problem

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A novel accelerated degradation test design considering stress optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8793-S8802页

作者： Shen, Dayong Liu, Tianyu Zhang, Longfei Wang, Jie Liu, Yumeng Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Chinese Acad Sci Inst Software Software Engn Beijing Peoples R China

Constant stress accelerated degradation tests (CSADT) are widely used in life perdition for highly reliable products to infer the lifetime distribution under operating conditions. Optimal design of an CSADT can improve life prediction accuracy and reduce test costs significantly. In the literature of CSADT design, most approaches focus on how to determine the sample allocation scheme, inspection frequency and test duration, but the issue of how to optimize the stress levels is seldom considered. In this work, we propose a novel method to optimize the CSADT considering both stress levels selection and samples allocation. First, an accelerated degradation model based on the Wiener process is used to model the degradation data. Next, under the constraint of sample size, a local-search based iterative algorithm is proposed to optimize parameters including stress levels and sample number under each level so as to obtain an accurate estimate of the distribution statistics. Finally, a case study of lithium-ion batteries is presented to validate the proposed method.

关键词： Degradation test design Stress optimization Wiener process local search algorithm

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An Evolutionary Variable Neighbourhood search for the Unrelated Parallel Machine Scheduling Problem

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IEEE ACCESS 2021年 9卷 42857-42867页

作者： Abdullah, Salwani Turky, Ayad Nazri, Mohd Zakree Ahmad Sabar, Nasser R. Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Malaysia Victoria Univ Coll Engn & Sci Melbourne Vic 3011 Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3083 Australia

This article addresses a challenging industrial problem known as the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times. In UPMSP, we have a set of machines and a group of jobs. The goal is to find the optimal way to schedule jobs for execution by one of the several available machines. UPMSP has been classified as an NP-hard optimisation problem and, thus, cannot be solved by exact methods. Meta-heuristic algorithms are commonly used to find sub-optimal solutions. However, large-scale UPMSP instances pose a significant challenge to meta-heuristic algorithms. To effectively solve a large-scale UPMSP, this article introduces a two-stage evolutionary variable neighbourhood search (EVNS) methodology. The proposed EVNS integrates a variable neighbourhood search algorithm and an evolutionary descent framework in an adaptive manner. The proposed evolutionary framework is employed in the first stage. It uses a mix of crossover and mutation operators to generate diverse solutions. In the second stage, we propose an adaptive variable neighbourhood search to exploit the area around the solutions generated in the first stage. A dynamic strategy is developed to determine the switching time between these two stages. To guide the search towards promising areas, a diversity-based fitness function is proposed to explore different locations in the search landscape. We demonstrate the competitiveness of the proposed EVNS by presenting the computational results and comparisons on the 1640 UPMSP benchmark instances, which have been commonly used in the literature. The experiment results show that our EVNS obtains better results than the compared algorithms on several UPMSP instances.

关键词： Switches Heuristic algorithms Space exploration Job shop scheduling Benchmark testing Optimization Licenses Machine scheduling problem genetic algorithm local search algorithm

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Optimisation approaches for supply chain planning and scheduling under demand uncertainty

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CHEMICAL ENGINEERING REsearch & DESIGN 2018年 138卷 341-357页

作者： Aguirre, Adrian M. Liu, Songsong Papageorgiou, Lazaros G. UCL Dept Chem Engn Ctr Proc Syst Engn Torrington Pl London WC1E 7JE England Swansea Univ Sch Management Bay CampusFabian Way Swansea SA1 8EN W Glam Wales

This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, local search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work. (C) 2018 The Authors. Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

关键词： Supply chain network Planning and scheduling under uncertainty MILP Model predictive control local search algorithm

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Parallel memetic structures

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INFORMATION SCIENCES 2013年 227卷 60-82页

作者： Caraffini, Fabio Neri, Ferrante Iacca, Giovanni Mol, Aran De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland Innovat Ctr Adv Sensors & Sensor Syst INCAS3 NL-9400 AT Assen Netherlands

Memetic Computing (MC) structures are algorithms composed of heterogeneous operators (memes) for solving optimization problems. In order to address these problems, this study investigates and proposes a simple yet extremely efficient structure, namely Parallel Memetic Structure (PMS). PMS is a single solution optimization algorithm composed of tree operators, the first one being a stochastic global search which explores the entire decision space searching for promising regions. In analogy with electrical networks, downstream of the global search component there is a parallel of two alternative elements, i.e. two local search algorithms with different features in terms of search logic, whose purpose is to refine the search in the regions detected by the upstream element. The first local search explores the space along the axes, while the second performs diagonal movements in the direction of the estimated gradient. The PMS algorithm, despite its simplicity, displays a respectable performance compared to that of popular meta-heuristics and modern optimization algorithms representing the state-of-the-art in the field. Thanks to its simple structure, PMS appears to be a very flexible algorithm for various problem features and dimensionality values. Unlike modern complex algorithm that are specialized for some benchmarks and some dimensionality values, PMS achieves solutions with a high quality in various and diverse contexts, for example both on low dimensional and large scale problems. An application example in the field of magnetic sensors further proves the potentials of the proposed approach. This study confirms the validity of the Ockham's Razor in MC: efficiently designed simple structures can perform as well as (if not better than) complex algorithms composed of many parts. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Memetic computing Memetic algorithm local search algorithm Rosenbrock algorithm Continuous optimization

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