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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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Train service design in an urban rail transit line incorporating multiple service routes and multiple train compositions

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2021年 123卷

作者： Li, Zhengyang Zhao, Jun Peng, Qiyuan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 611756 Sichuan Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

This paper focuses on the train service design problem within a given period in an urban rail transit line, where multiple either full-length or short-turn service routes can be operated, and each service route can utilize one of several different train compositions. The problem lies on determining the turn-back stations, train composition and frequency of each service route operated on the line. Considering the interests of operators and passengers, we decompose the problem as two subproblems namely train service configuration and passenger assignment. The first subproblem is formulated as an integer linear programming model with the objective of minimizing operators' cost. Given a train service scheme, the second subproblem is modelled as a capacitated continuous multi-commodity flow model to minimize passengers' waiting time cost and transfer cost. The optimal strategy is extended to determine the behaviour of passengers and capture the extra waiting time of passengers under capacity constraint. The two sub-models are weighted and integrated into a mixed integer nonlinear programming model, which is further transformed into a mixed integer linear programming model using a novel linearization method. By exploiting the special characteristics of the model, a tailored and easy to implement local search algorithm is developed to solve large-scale instances. Starting from the operator-optimum solution which can be easily obtained, the algorithm solves the two sub-models iteratively to search better solutions within a precalculated search range which is smaller than the complete feasible domain. Finally, different sizes of instances constructed from two urban rail transit lines are utilized to demonstrate the performance and practicability of the proposed approaches.

关键词： Urban rail transit Train service design Multiple service routes Multiple train compositions local search algorithm

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Heuristic approaches for the family traveling salesman problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL REsearch 2021年第1期28卷 262-295页

作者： Bernardino, Raquel Paias, Ana Univ Lisbon Fac Ciencias DEIO C6Piso 4 P-1749016 Lisbon Portugal Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper C6Piso 1 P-1749016 Lisbon Portugal

In this article, we address the family traveling salesman problem (FTSP), an NP-hard problem that may be seen as a generalization of the traveling salesman problem. In the FTSP, the set of cities is partitioned into several families and one wants to find the minimum cost route that visits a given number of cities in each family. We propose two metaheuristics, a population-based method and a local search method, and a hybrid algorithm, which combines a branch-and-cut algorithm with a local search procedure. All the proposed methods improve the best known upper bounds from the literature. The local search method and the hybrid algorithm improve the best known upper bounds for all the benchmark instances with unknown optimal value, while the population-based method improves the best known upper bounds for all instances, except two. Furthermore, we developed an instance generator to create additional test instances with different visit patterns. These newly generated instances were considered in the computational experiment and, thus, we provide optimal values or upper bounds for each instance. Additionally, we created a website dedicated to the FTSP, where this information is made available to the scientific community ().

关键词： family traveling salesman problem genetic algorithm local search algorithm hybrid algorithm

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Fractal triangular search: a metaheuristic for image content search

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IET IMAGE PROCESSING 2018年第8期12卷 1475-1484页

作者： Rodrigues, Erick O. Liatsis, Panos Satoru, Luiz Conci, Aura Univ Fed Fluminense Dept Comp Sci Niteroi RJ Brazil Univ Fed Itajuba UNIFEI Dept Comp Sci Itabira MG Brazil Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates

This work proposes a variable neighbourhood search (FTS) that uses a fractal-based local search primarily designed for images. searching for specific content in images is posed as an optimisation problem, where evidence elements are expected to be present. Evidence elements improve the odds of finding the desired content and are closely associated to it in terms of spatial location. The proposed local search algorithm follows the fashion of a chain of triangles that engulf each other and grow indefinitely in a fractal fashion, while their orientation varies in each iteration. The authors carried out an extensive set of experiments, which confirmed that FTS outperforms state-of-the-art metaheuristics. On average, FTS was able to locate content faster, visiting less incorrect image locations. In the first group of experiments, FTS was faster in seven out of nine cases, being >8% faster on average, when compared to the second best search method. In the second group, FTS was faster in six out of seven cases, and it was >22% faster on average when compared to the approach ranked second best. FTS tends to outperform other metaheuristics substantially as the size of the image increases.

关键词： search problems novel metaheuristic FTS specific content incorrect image locations Optimisation techniques Computer vision and image processing techniques evidence elements CT images Optical, image and video signal processing local search algorithm searching fractal triangular search fractal fashion desired content search method optimisation image increases variable neighbourhood search state-of-the-art metaheuristics Combinatorial mathematics fractal-based local search image content search fractals

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Sparsity-aware, channel order-blind pilot placement with channel estimation in orthogonal frequency division multiplexing systems

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IET COMMUNICATIONS 2015年第9期9卷 1182-1190页

作者： Peng, Xiaoqing Wu, Weimin Sun, Jun Liu, Yingzhuang Li, Frank Yong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China Univ Agder Dept Informat & Commun Technol N-4898 Grimstad Norway

Equispaced pilot arrangement is the most popular scheme for pilot-aided transmission in orthogonal frequency division multiplexing systems. In this study, the authors argue that a non-equispaced pilot pattern may outperform its equispaced counterpart, if they fully take into account the sparsity of the channel impulse response which is inherent in wireless channels. More specifically, a sparsity-aware pilot arrangement scheme based on the coherence criterion is investigated in this study. To address the resulting non-deterministic polynomial (NP)-hard combinatorial optimisation problem, they propose an efficient local search algorithm. For channel estimation, they convert it to a sparse recovery problem. To enhance the applicability of the authors scheme, that is, when there is no prior knowledge about the channel order, they propose to employ the Bayesian information criterion to estimate the channel order first and then recover the sparse channel vector via existing low-complexity methods, for example, orthogonal matching pursuit. By combining the above pilot arrangement scheme with channel estimation, their scheme exhibits substantially better performance in comparison with the conventional equispaced schemes with linear (or spline) interpolation, in terms of total number of pilot symbols and bit error rate.

关键词： Optimisation techniques sparsity-aware orthogonal frequency division multiplexing system search problems channel impulse response Modulation and coding methods channel order-blind pilot placement Bayesian information criterion wireless channels OFDM modulation channel estimation Combinatorial mathematics Bayes methods NP-hard combinatorial optimisation problem orthogonal matching computational complexity transient response sparse recovery problem local search algorithm Radio links and equipment Communication channel equalisation and identification Other topics in statistics

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Data-independent neighborhood functions and strict local optima

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DISCRETE APPLIED MATHEMATICS 2005年第3期146卷 233-243页

作者： Armstrong, DE Jacobson, SH BAE Syst Arlington VA 22203 USA Univ Illinois Dept Mech & Ind Engn Urbana IL 61801 USA

The paper proves that data-independent neighborhood functions with the smooth property (all strict local optima are global optima) for maximum 3-satisfiability (MAX 3-SAT) must contain all possible solutions for large instances. Data-independent neighborhood functions with the smooth property for 0-1 knapsack are shown to have size with the same order of magnitude as the cardinality of the solution space. Data-independent neighborhood functions with the smooth property for traveling salesman problem (TSP) are shown to have exponential size. These results also hold for certain polynomially solvable sub-problems of MAX 3-SAT, 0-1 knapsack and TSP. (C) 2004 Elsevier B.V. All rights reserved.

关键词： computational complexity local search algorithm exponential neighborhood

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A PAM approach to handling disruptions in real-time vehicle routing problems

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DECISION SUPPORT SYSTEMS 2013年第3期54卷 1380-1393页

作者： Hu, Xiangpei Sun, Lijun Liu, Linlin Dalian Univ Technol Sch Management & Econ Inst Syst Engn Dalian 116023 Liaoning Provin Peoples R China

During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based modeling approach, PAM (disruption-handling Policies, local search algorithms and object-oriented Modeling), is developed, which combines the scheduling knowledge of experienced schedulers with the optimization knowledge concerning models and algorithms in the field of Operations Research to obtain an effective solution in real time. Experienced schedulers can respond to different disruptions promptly with heuristic adjustment based on their experience, but their solutions may be inaccurate, inconsistent, or even infeasible. This method is limited when the problem becomes large-scale. The model-algorithm method can handle large-scale problems, but it has to predefine a specific disruption and a specific distribution state for constructing a model and algorithm, which is inflexible, time-consuming and consequently unable to promptly obtain solutions for responding to different disruptions in real time. PAM modeling approach combines the advantages and eliminates the disadvantages of the two methods aforementioned. Computational experiments show that solutions achieved by this modeling approach are practical and the speed of achieving the solutions is fast enough for responding to disruptions in real time. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Knowledge representation Object-oriented modeling local search algorithm Disruption Real-time Vehicle Routing Problem (RVRP)

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Clustering of search trajectory and its application to parameter tuning

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JOURNAL OF THE OPERATIONAL REsearch SOCIETY 2013年第12期64卷 1742-1752页

作者： Lindawati Lau, H. C. Lo, D. Singapore Management Univ Singapore 178902 Singapore

This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parameter tuning. The key idea is to use the search trajectory as a generic feature for clustering problem instances. The advantage of using search trajectory is that it can be obtained from any local-search based algorithm with small additional computation time. We explore and compare two different search trajectory representations, two sequence alignment techniques (to calculate similarities) as well as two well-known clustering methods. We report experiment results on two classical problems: Travelling Salesman Problem and Quadratic Assignment Problem and industrial case study.

关键词： generic feature search trajectory instance-based automated parameter tuning sequence alignment local search algorithm

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Attractive force search algorithm for piecewise convex maximization problems

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OPTIMIZATION LETTERS 2012年第7期6卷 1317-1333页

作者： Fortin, Dominique Tseveendorj, Ider Univ Versailles Lab PRiSM F-78035 Versailles France INRIA F-78153 Le Chesnay France

We consider mathematical programming problems with the so-called piecewise convex objective functions. A solution method for this interesting and important class of nonconvex problems is presented. This method is based on Newton's law of universal gravitation, multicriteria optimization and Helly's theorem on convex bodies. Numerical experiments using well known classes of test problems on piecewise convex maximization, convex maximization as well as the maximum clique problem show the efficiency of the approach.

关键词： Global search algorithm local search algorithm Nonconvex optimization Convex maximization Piecewise convex maximization

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Piece adding technique for convex maximization problems

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JOURNAL OF GLOBAL OPTIMIZATION 2010年第4期48卷 583-593页

作者： Fortin, Dominique Tseveendorj, Ider Univ Versailles Lab PRiSM F-78035 Versailles France INRIA F-78153 Le Chesnay France

In this article we provide an algorithm, where to escape from a local maximum y of convex function f over D, we (locally) solve piecewise convex maximization max{min{f (x) - f (y), p (y) (x)} | x is an element of D} with an additional convex function p (y) (center dot). The last problem can be seen as a strictly convex improvement of the standard cutting plane technique for convex maximization. We report some computational results, that show the algorithm efficiency.

关键词： Global search algorithm local search algorithm Nonconvex optimization Convex maximization Piecewise convex maximization

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