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A novel differential evolution mapping technique for generic combinatorial optimization problems

APPLIED SOFT COMPUTING 2019年 80卷 297-309页

作者： Ali, Ismail M. Essam, Daryl Kasmarik, Kathryn Univ New South Wales SEIT Canberra ACT Australia

Differential evolution is primarily designed and used to solve continuous optimization problems. Therefore, it has not been widely considered as applicable for real-world problems that are characterized by permutation-based combinatorial domains. Many algorithms for solving discrete problems using differential evolution have been proposed, some of which have achieved promising results. However, to enhance their performance, they require improvements in many aspects, such as their convergence speeds, computational times and capabilities to solve large discrete problems. In this paper, we present a new mapping method that may be used with differential evolution to solve combinatorial optimization problems. This paper focuses specifically on the mapping component and its effect on the performance of differential evolution. Our method maps continuous variables to discrete ones, while at the same time, it directs the discrete solutions produced towards optimality, by using the best solution in each generation as a guide. To judge its performance, its solutions for instances of well-known discrete problems, namely: 0/1 knapsack, traveling salesman and traveling thief problems, are compared with those obtained by 8 other state-of-the-art mapping techniques. To do this, all mapping techniques are used with the same differential evolution settings. The results demonstrated that our technique significantly outperforms the other mapping methods in terms of the average error from the best-known solution for the traveling salesman problems, and achieves promising results for both the 0/1 knapsack and the traveling thief problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Discrete differential evolution combinatorial optimization problems 0/1 knapsack problems Traveling salesman problems Traveling thief problems

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A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems

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INFORMATION SCIENCES 2022年 596卷 1-14页

作者： Dong, Jianping Zhang, Gexiang Luo, Biao Yang, Qiang Guo, Dequan Rong, Haina Zhu, Ming Zhou, Kang Chengdu Univ Technol Sch Nucl Technol & Automat Engn Chengdu 610059 Peoples R China Chengdu Univ Technol Res Ctr Artificial Intelligence Chengdu 610059 Peoples R China Chengdu Univ Informat Technol Sch Control Engn Chengdu 610225 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China Wuhan Polytech Univ Sch Dept Econ & Management Wuhan 430023 Peoples R China

An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators of evo-lutionary algorithms or swarm intelligence algorithms. To develop the promising and sig-nificant research direction, this paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) with a distributed population structure and a new adaptive learning rate considering population diversity. Extensive experiments on knapsack prob-lems show that DAOSNPS gains much better solutions than OSNPS, adaptive optimization spiking neural P system, genetic quantum algorithm and novel quantum evolutionary algo-rithm. Population diversity and convergence analysis indicate that DAOSNPS achieves a better balance between exploration and exploitation than OSNPS and AOSNPS. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Membrane computing Spiking neural P system optimization spiking neural P system combinatorial optimization problems

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Cones of multipowers and combinatorial optimization problems

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2013年第5期53卷 647-654页

作者： Vyalyi, M. N. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia

The cone of multipowers is dual to the cone of nonnegative polynomials. The relation of the former cone to combinatorial optimization problems is examined. Tensor extensions of polyhedra of combinatorial optimization problems are used for this purpose. The polyhedron of the MAX-2-CSP problem (optimization version of the two-variable constraint satisfaction problem) of tensor degree 4k is shown to be the intersection of the cone of 4k-multipowers and a suitable affine space. Thus, in contrast to SDP relaxations, the relaxation to a cone of multipowers becomes tight even for an extension of degree 4.

关键词： combinatorial optimization problems cones of multipowers tensor extensions of polyhedra

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Estimating attraction basin sizes of combinatorial optimization problems

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2018年第4期7卷 369-384页

作者： Elorza, Anne Hernando, Leticia Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence San Sebastian 20018 Spain Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Architecture & Technol San Sebastian 20018 Spain

Given a particular instance of a combinatorial optimization problem, the knowledge about the attraction basin sizes can help to analyze the difficulty encountered by local search algorithms while solving it. As calculating these sizes exhaustively is computationally intractable, we focus on methods for their estimation. The accuracy of some of these estimation methods depends on the way in which the sample of solutions of the search space is chosen. In this paper, we propose a novel sampling method, which incorporates the knowledge obtained by the already explored solutions into the sampling strategy. So, in contrast to those that already exist, our method can adapt its behavior to the characteristics of the particular attraction basin. We apply our proposal to a number of instances of three famous problems: the quadratic assignment problem, the linear ordering problem and the permutation flow shop scheduling problem. We consider permutation sizes n = 10 and n = 12 and three different neighborhoods: adjacent swap, 2-exchange and insert, and observe that the new method generally outperforms those that already exist.

关键词： combinatorial optimization problems Neighborhoods Attraction basins Local optima Local search algorithms Estimators

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An Ising Model-Based Annealing Processor With 1024 Fully Connected Spins for combinatorial optimization problems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第8期70卷 3074-3078页

作者： Huang, Zhanhong Jiang, Dong Wang, Xiangrui Yao, Enyi South China Univ Technol Sch Microelect Guangzhou 511442 Peoples R China

This brief presents a novel annealing processor (AP) design with 1024 fully-connected spins based on a modified Ising model annealing algorithm for combinatorial optimization problems. By adopting the proposed Turbo code-based interleaved random sequence generator (TCSG) and multi-spin update method, the memory usage is made considerable reduction and multi-spin parallel update is supported. The prototype is implemented using FPGA with the operation frequency of 100 MHz. We tested our design on various G-set problems with an average cut accuracy of 99.19% achieved. The proposed design outperforms the CPU-based method by achieving a max speedup of 1099x.

关键词： Ising model simulated annealing fully connected combinatorial optimization problems

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A Hybrid Parallel Search Algorithm for Solving combinatorial optimization problems on Multicore Clusters 17th

A Hybrid Parallel Search Algorithm for Solving Combinatorial...

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17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. The results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. These improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： Parallel search algorithms Hybrid programming Multicore cluster combinatorial optimization problems Hash Distributed A*

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The Effectiveness of Hybrid Negative Correlation Learning in Evolutionary Algorithm for combinatorial optimization problems

The Effectiveness of Hybrid Negative Correlation Learning in...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Sirovetnukul, R. Chutima, P. Wattanapornprom, W. Chongstitvatana, P. Mahidol Univ Dept Ind Engn Nakhon Pathom Thailand Chulalongkorn Univ Dept Ind Engn Bangkok Thailand Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781457707391

Most evolutionary algorithms optimize the information from good solutions found in the population. A selection method discards the below-average solutions assuming that they do not contribute any information to update the probabilistic models. This work develops an algorithm called Coincidence algorithm (COIN) which merges negative correlation learning into the optimization process. A knight's tour problem, one of NP-hard multimodal Hamiltonian path problems, is tested with COIN. The results show that COIN is a competitive algorithm in converging to better solutions and maintaining diverse solutions to solve combinatorial optimization problems.

关键词： Negative knowledge Coincidence algorithm combinatorial optimization problems

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An Annealing Processor based on 1k-Spin Fully-Connected Ising Model for combinatorial optimization problems 56

An Annealing Processor based on 1k-Spin Fully-Connected Isin...

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Huang, Zhanhong Wang, Xiangrui Jiang, Dong Huang, Yukang Yao, Enyi South China Univ Technol Sch Microelect Guangzhou 511442 Peoples R China Minist Educ Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

ISBN: (纸本)9781665451093

combinatorial optimization problems (COPs) find extensive applications in industrial and social scenarios such as transportation and communication. As the size of NP-hard COPs increases, it becomes impossible to obtain the optimal solution using an enumerative method. Recently, Ising model based annealing processors have received increasing attention due to their potential for rapidly converging to the near-optimal solutions after mapping the problem to them. This paper presents a novel annealing processor (AP) with 1024 fully-connected spins based on a modified Ising model annealing algorithm, which is more suitable for hardware implementation compared to conventional simulated annealing (SA) algorithm. The prototype is implemented using FPGA with the operation frequency up to 100MHz. We tested our design on various G-set problems with an average cut accuracy of 99.19% achieved. The proposed design outperforms the conventional CPU-based method by achieving a max speedup of 2204x for G51.

关键词： Ising model simulated annealing fully connected combinatorial optimization problems

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Performance Improvement of Heuristic Algorithms for Large Scale combinatorial optimization problems using Lebesgue Spectrum Filter

Performance Improvement of Heuristic Algorithms for Large Sc...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kato, Tomohiro Hasegawa, Mikio Aihara, Kazuyuki Tokyo Univ Sci Dept Elect Engn Chiyoda Ku 1-14-6 Kudankita Tokyo 162 Japan Univ Tokyo Inst Ind Sci Meguro Ku Tokyo 1538505 Japan

ISBN: (纸本)9781467314909

In this paper, we analyze effectiveness of a new metaheuristic approach, which utilizes ideal spatio-temporal chaotic dynamics generated by Lebesgue Spectrum Filter (LSF). In the previous researches on the additive chaotic noise to the heuristic searches for combinatorial optimization problems, it has been shown that the chaotic sequences with negative autocorrelation improve the performance of asynchronously updated algorithms, such as mutually connected neural networks, asynchronous heuristic searches and so on. The effectiveness of chaos can be understood by the conventional theory of the chaotic CDMA, which showed that the cross-correlation between the sequences with negative autocorrelation becomes lowest. The spatio-temporal chaotic searching dynamics with such lowest cross-correlation has been shown effective to improve asynchronously updated combinatorial optimization algorithms. In this paper, as such asynchronously updated combinatorial optimization algorithms, we introduce the 2-exchange method, the k-opt method, the Lin-Kernighan method, and the Or-opt method, and improve the performance of them by applying the LSF. Our numerical simulation results show that the performances of all above heuristic methods could be improved by using negative autocorrelation.

关键词： component combinatorial optimization problems k-opt Lin-Kernighan Or-opt Nonlinear Dynamics Lebesgue Spectrum Filter

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An Intelligent Algorithm Based on Neural Network for combinatorial optimization problems 7

An Intelligent Algorithm Based on Neural Network for Combina...

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7th International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Zhang, Cui Li, Qiang Wang, Ronglong Liaoning Inst Sci & Technol Dept Autocontrol Benxi Peoples R China Benxi Urban Planning & Design Inst Benxi Peoples R China Univ Fukui Fac Engn Fukui 910 Japan

In this paper, we construct a Hopfield-type neural network to solve combinatorial optimization problems which is very important to medicine and biology. We applied the neural network to the four-coloring map problems ... 详细信息

ISBN: (纸本)9781479958382

关键词： Intelligent algorithm binary neuron model combinatorial optimization problems Four-coloring map problems

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