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COMPUTERS & OPERATIONS RESEARCH 2012年第1期39卷 42-47页

作者： Boyer, V. El Baz, D. Elkihel, M. CNRS LAAS F-31077 Toulouse France Univ Toulouse UPS INSA INPISAELAAS F-31077 Toulouse France

A parallel implementation via CUDA of the dynamic programming method for the knapsack problem on NVIDIA GPU is presented. A GTX 260 card with 192 cores (1.4 GHz) is used for computational tests and processing times obtained with the parallel code are compared to the sequential one on a CPU with an Intel Xeon 3.0 GHz. The results show a speedup factor of 26 for large size problems. Furthermore, in order to limit the communication between the CPU and the CPU, a compression technique is presented which decreases significantly the memory occupancy. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： combinatorial optimization problems Dense dynamic programming Parallel computing GPU computing CUDA

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Using GPU Computing for Solving the Two-Dimensional Guillotine Cutting Problem

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INFORMS JOURNAL ON COMPUTING 2016年第3期28卷 540-552页

作者： Boschetti, Marco A. Maniezzo, Vittorio Strappaveccia, Francesco Univ Bologna Dept Math I-47521 Cesena Italy Univ Bologna Dept Comp Sci & Engn DISI I-47521 Cesena Italy

In recent years, GPU computing has become an increasingly important tool to develop efficient applications in several areas, including optimization. One of the optimization approaches that seems to take most advantage from GPU computing is dynamic programming. In this paper, we investigate the application of GPU computing to the two-dimensional guillotine cutting problem, solved by dynamic programming. We show a possible implementation and we discuss a number of technical issues. Computational results on test instances available in the literature and on new larger instances show the effectiveness of the dynamic programming approach based on GPU computing for this problem.

关键词： combinatorial optimization problems cutting problems dynamic programming parallel computing GPU computing CUDA

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Ant Colony optimization with Genetic Operation and Its Application to Traveling Salesman Problem

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2009年第5期E92A卷 1368-1372页

作者： Wang, Rong-Long Zhou, Xiao-Fan Okazaki, Kozo Univ Fukui Fac Engn Fukui 9108507 Japan

Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.

关键词： ant colony optimization combinatorial optimization problems traveling salesman problem genetic algorithm genetic operation

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Parameter tuning of continuous Hopfield network applied to combinatorial optimization

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2024年第2期92卷 257-275页

作者： Rbihou, Safae Joudar, Nour-Eddine Haddouch, Khalid Sidi Mohamed Ben Abdellah Univ Engn Syst & Applicat Fes Morocco Mohammed V Univ Rabat Res Ctr STIS Dept Appl Math & Informat ENSAMM2CS Rabat Morocco

The continuous Hopfield network (CHN) has provided a powerful approach to optimization problems and has shown good performance in different domains. However, two primary challenges still remain for this network: defining appropriate parameters and hyperparameters. In this study, our objective is to address these challenges and achieve optimal solutions for combinatorial optimization problems, thereby improving the overall performance of the continuous Hopfield network. To accomplish this, we propose a new technique for tuning the parameters of the CHN by considering its stability. To evaluate our approach, three well-known combinatorial optimization problems, namely, weighted constraint satisfaction problems, task assignment problems, and the traveling salesman problem, were employed. The experiments demonstrate that the proposed approach offers several advantages for CHN parameter tuning and the selection of optimal hyperparameter combinations.

关键词： Continuous Hopfield network Parameter settings Hyperparameters tuning combinatorial optimization problems Equilibrium point

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Development of DEPSO Island Model with Particle Speed Limit for Distribution Network Reconfigurations

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IFAC-PapersOnLine 2018年第28期51卷 552-557页

作者： Ikegami, Hiromitsu Mori, Hiroyuki Department of Network Design Meiji University Tokyo Japan

This paper presents a new metaheuristic method for distribution network reconfigurations. In recent years, distribution network reconfigurations play an important role to reduce the network loss with sectionalizing switches and shunt capacitors. Distribution network reconfigurations may be formulated as a combinatorial optimization of the NP hard problem. In this paper, Parallel Discrete Evolutionary Particle Swarm optimization (PDEPSO) is proposed to deal with distribution network reconfigurations. PDEPSO is Discrete Particle Swarm optimization (DEPSO) with the island model of parallel computation in which DEPSO integrates EPSO of adaptive PSO with the sigmoid function to handle combinatorial optimization problems. Furthermore, to improve the performance of PDEPSO, this paper develops a method that sets the particle speed limit of DEPSO to maintain the solution diversity and to evaluate better solutions. The effectiveness of the proposed method is demonstrated in the IEEE 69-node distribution system. © 2018

关键词： Particle swarm optimization (PSO) Artificial intelligence combinatorial optimization Computational complexity Electric power distribution Global optimization combinatorial optimization problems Discrete particle swarm optimization Distribution automation Distribution feeders Distribution network reconfiguration Evolutionary particle swarm optimizations Loss minimization Parallel Computation

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A collaborative neurodynamic optimization algorithm to traveling salesman problem

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1809-1821页

作者： Zhong, Jing Feng, Yuelei Tang, Shuyu Xiong, Jiang Dai, Xiangguang Zhang, Nian Chongqing Three Gorges Univ Chongqing Municipal Inst Higher Educ Key Lab Intelligent Informat Proc & Control Chongqing 404120 Peoples R China Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA

This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with n x n neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the convergence condition of CHNN and solve TSP by CHNN. Finally, a population of CHNNs is used to search for local optimal solutions of TSP and the globally optimal solution is obtained using particle swarm optimization. Experimental results show the effectiveness of the CNO approach for solving TSP.

关键词： combinatorial optimization problems Collaborative neurodynamic optimization Hopfield neural network Traveling salesman problem

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A Methodology of the Mathematical Modeling for Perspective Development of Nodes and Transport Routes in the Multicommodity Hierarchical Network. I. optimization problems

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CYBERNETICS AND SYSTEMS ANALYSIS 2024年第1期60卷 72-83页

作者： Vasyanin, V. A. Trofymchuk, O. M. Ushakova, L. P. Natl Acad Sci Ukraine Inst Telecommun & Global Informat Space Kiev Ukraine

The paper proposes a methodology for mathematical modeling of the phased development of nodes and transport routes in a hierarchical network with multicommodity discrete flows of correspondences based on solving the problems of optimizing its structure and distribution of flows. As a rule, such networks consist of a decentralized mainline network and networks in the internal service areas of mainline nodes. In a multicommodity network, each node can exchange correspondence (products, goods, cargo, messages) with the other nodes. Correspondence is characterized by a source node, a sink node, and a quantity, which for data transmission networks is given by the number of bytes, kilobytes, etc., and for transport networks by the number of cargo units in a package of uniform size. In the mainline network, all correspondences are transmitted via communication channels or transported by vehicles in transport units of a given size (capacity, volume). The authors considered the main postulates of generating a mathematical model of the perspective development of mainline network. They presented a method of mathematical modeling of the phased development of nodes and transport routes, which includes, for each stage of development, the forecasting of data and network parameters, solving the problem of packing correspondence and choosing the structure of the network, and solving the problem of distribution and routing of transport unit flows.

关键词： multicommodity hierarchical networks discrete flows combinatorial optimization problems mathematical models computer simulation

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combinatorial test list generation based on Harmony Search Algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期13卷 3361-3377页

作者： Alsewari, AbdulRahman A. Poston, Robin Zamli, Kamal Z. Balfaqih, Mohammed Aloufi, Khalid S. Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pahang 26300 Gambang Malaysia Univ Malaysia Pahang IBM Ctr Excellence Pahang 26300 Gambang Malaysia Univ Memphis Business Informat & Technol Memphis TN 38152 USA Jeddah Univ Comp & Networks Engn Dept Jeddah 23218 Saudi Arabia South Ural State Univ Automobile Transportat Dept Chelyabinsk 454080 Russia Taibah Univ Coll Comp Sci & Engn Medina 41477 Saudi Arabia

combinatorial test case generation faces a problem on how to reduce the test cases by uncover the unnecessary test cases. So, there is a need for expert applications or strategies that generate the most optimum test cases keeping in mind the most important combinations. Complementing existing work on combinatorial test case generation strategies, also known as t-way testing strategies (i.e., where t represents interaction degree), this paper presents the design and the implementation of a new combinatorial test list generation strategy based on Harmony Search (HS) algorithm, called General T-way Harmony Search-based Strategy (GTHS). HS has been chosen to be the main engine of test generation because it could balance between intensification and diversification. Benchmarking experimental results show that GTHS produces competitive results as compared to other existing well-known optimization-based strategies and provides the support for high combination degrees (i.e., t <= 12).

关键词： Computational ontelligence Intelligent test list generators optimization algorithms combinatorial optimization problems

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A new updating procedure in the Hopfield-type network and its application to N-Queens problem

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2002年第10期E85A卷 2368-2372页

作者： Wang, RL Tang, Z Cao, QP Toyama Univ Fac Engn Toyama 9308555 Japan Tateyama Syst Inst Toyama 9300001 Japan

When solving combinatorial optimization problems with a binary Hopfield-type neural network, the updating process in neural network is an important step in achieving a solution. In this letter, we propose a new updating procedure in binary Hopfield-type neural network for efficiently solving combinatorial optimization problems. In the new updating procedure, once the neuron is in excitatory state, then its input potential is in positive saturation where the input potential can only be reduced but cannot be increased, and once the neuron is in inhibitory state;then its input potential is in negative saturation where the input potential can only be increased but cannot be reduced. The new updating procedure is evaluated and compared with the original procedure and other improved methods through simulations based on N-Queens problem. The results show that the new updating procedure improves the searching capability of neural networks with shorter computation time. Particularly, the simulation results show that the performance of proposed method surpasses the exiting methods for N-queens problem in synchronous parallel computation model.

关键词： combinatorial optimization problems binary Hopfield-type neural network updating procedure synchronous parallel computation model N-Qeens problem

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A hybrid genetic algorithm to solve a multi-objective Pickup and Delivery Problem

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IFAC-PapersOnLine 2017年第1期50卷 14656-14661页

作者： Chami, Z. Al Manier, H. Manier, M.-A. Fitouri, C. Univ. Bourgogne Franche-Comté UTBM OPERA BelfortF-90010 France Univ. Bourgogne Franche-Comté FEMTO-ST institute ENSMM CNRS Besançon25000 France Université de Tunis ENSIT LR13ES03 SIME Montfleury1008 Tunisia

The Pickup and Delivery Problem, known as PDP, is one of the most combinatorial optimization problems studied in the literature. In this type of problems, loads must be transported by a fleet of vehicles from pickup sites to delivery sites. A set of constraints must be respected in relation with the capacity of the vehicles, the opening and closing times of each site. This paper presents the first metaheuristic method to solve a new variant of the PDP which we called SPDPTWPD (Selective PDP with Time Windows and Paired Demands). In this variant, the precedence constraints (paired demands) and the choice of sites to be served (selective aspect) must be considered. We proposed a hybrid genetic algorithm to deal with the multi-objective SPDPTWPD. We tested our proposed approach on benchmark instances and the obtained results show its efficiency. © 2017

关键词： Urban transportation Benchmarking combinatorial optimization Fleet operations Genetic algorithms Pickups Transportation combinatorial optimization problems Hybrid genetic algorithms Meta heuristic approach Meta heuristic methods Pickup and delivery problems Precedence constraints selective PDPTW Urban logistics

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