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ant colony optimization algorithms for Digital Device Diagnostics

AUTOMATIC CONTROL AND COMPUTER SCIENCES 2015年第2期49卷 82-87页

作者： Speranskii, D. V. Moscow State Univ Railway Engn Ul Chasovaya 22-2 Moscow 125993 Russia

This paper investigates the possibility of solving two problems of digital device diagnostics by means of ant colony optimization. It is shown how these problems can be reduced to the classical traveling salesman and ... 详细信息

关键词： ant colony optimization algorithms digital devices diagnostics

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Multi-GPU accelerating strategies of ant colony optimization algorithms using Rank Based and Strong Elitist versions 35

Multi-GPU accelerating strategies of Ant Colony Optimization...

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35th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Avila, Andres I. Manuel Aedo, Juan Gonzalez-Flores, Mariela Univ La Frontera Dept Ingn Matemat Temuco Chile Univ Aysen Dept Cs Nat & Tecnol Coyhaique Chile

ISBN: (纸本)9798350381603

The most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optimization techniques to solve this problem. Although there is a zoo of possibilities, ant colony optimization (ACO) is still one of the most efficient, parallel, and simple techniques to implement. The pheromone evaporation rate, alpha,beta, and ant number M are the four parameters fitted for finding the best solutions. Candidate list and the use of a source solution are efficient strategies to optimize large problems, but there are other strategies to improve quality solutions such as local search strategies. Among the local search techniques, k-opt search has proved to be very efficient to deal with path-crossing. The initial route is split into k sub-routes connected in (k- 1)!2(k-1) ways. Thus, 3-opt is an efficient strategy balancing complexity and precision. Moreover, the best alpha and beta are the result of the used strategy. Finally, the solution is improved by accelerating through parallel versions with multiGPUs. In this article, four ACO algorithms were developed using two variations (Rank Based 3-opt and Strong Elitist 3-opt), each one with two strategies (candidate list and restricted). To test the algorithms, TSPLIB95 was used with test problems between N = 51 and 4, 461 nodes in a server with 4 RTX A4000 GPUs. After improving the algorithms and parallelization, the parameters are tuned to get the highest performance improving the local minimum search. Statistical analysis of repetitions shows good stability of the chosen metaheuristics.

关键词： Multi-GPU ant colony optimization algorithms metaheuristics local search high performance

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A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

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BIOINSPIRATION & BIOMIMETICS 2014年第3期9卷 036006-36005页

作者： Zhang, Zili Gao, Chao Liu, Yuxin Qian, Tao Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia

ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

关键词： Physarum ploycephalum Physarum-inspired model ant colony optimization algorithms travelling salesman problem

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A Clustering Routing Algorithm Based on Improved ant colony optimization algorithms for Underwater Wireless Sensor Networks

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algorithms 2020年第10期13卷 250-250页

作者： Xiao, Xingxing Huang, Haining Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Sci & Technol Adv Underwater Acoust Signa Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.

关键词： underwater wireless sensor networks ant colony optimization algorithms clustering routing algorithms energy efficiency network lifetime

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Arrangement optimization of hammers and fenders on Scrap Metal Shredder using ant colony algorithms

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JOURNAL OF VIBRATION AND CONTROL 2012年第5期18卷 659-670页

作者： Wang, Guoqiang Zhao, Kaijun Li, Xuefei Li, Li Wang, Jixin Jilin Univ Coll Mech Sci & Engn Changchun 130022 Peoples R China Shenyang N Heavy Ind Shenyang Peoples R China Changchun Bus Factory Jilin Peoples R China

A method of optimizing the arrangement of hammers and fenders on a Scrap Metal Shredder is presented. It offers a means of reducing dynamic unbalance force. A dynamic balance model is established for analyzing rotor unbalance force. The model is verified by software simulation. ant colony optimization algorithms (ACOA) are then introduced to arrange hammers and fenders. Meanwhile, ACOA has been improved by introducing a multi-dimensional matrix and random number q(0) and separating the first loop from other loops. A rational arrangement of hammers and fenders is obtained. The studies show that using ACOA to arrange hammers and fenders can significantly reduce the dynamic unbalance force. For unbalance force of the rotor, the maximum reduction can be up to 92.6% compared with the original arrangement before optimization.

关键词： Arrangement of hammers and fenders dynamic balance ant colony optimization algorithms Scrap Metal Shredder

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Grid ant colony optimization Applied to a Multi-Robotic Garbage Collection System 2

Grid Ant Colony Optimization Applied to a Multi-Robotic Garb...

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2nd Brazilian Robotics Symposium (SBR) / 11th Latin American Robotics Symposium (LARS) / 6th Robocontrol Workshop on Applied Robotics and Automation

作者： Sales, Daniel O. Dias, Mauricio A. Osorio, Fernando S. Univ Sao Paulo Inst Math Sci & Computat ICMC Mobile Robot Lab LRM Sao Carlos SP Brazil

ISBN: (纸本)9781479967117

Garbage recycling and collection problem is an interesting problem that researchers are applying swarm intelligence algorithms to solve. Some previous approaches used particle swarm optimization, immune systems and ant colony optimization algorithms and achieved good results. ant colony optimization is a well-known swarm intelligence algorithm that is normally used to solve computational problems which can be reduced to finding good paths in graphs. A multi-robotic system can be applied to solve this problem but it will need a control algorithm to accomplish the task. Applying the regular ant colony optimization algorithm to control the multi-robotic system is not a trivial task due to the graph representation needed. This work proposes modifications in the ant colony optimization algorithm that uses grid representation and applies the modified algorithm to solve this problem. The results showed a decrease of one order of magnitude in the number of iterations needed to solve the problem compared to the previous version of the algorithm. Considering the results the proposed algorithm showed to be able to control a multi-robotic system for the chosen problem.

关键词： ant colony optimisation mobile robots multi-robot systems refuse disposal ant colony optimization algorithms computational problems garbage collection problem garbage recycling grid ant colony optimization immune systems multirobotic garbage collection system multirobotic system particle swarm optimization swarm intelligence algorithms Algorithm design and analysis ant colony optimization Control systems Heuristic algorithms Particle swarm optimization Recycling Robots ACO Evolvable Systems Grid ACO Multi-Robotics Robotics Particle swarm optimization ant colony optimization Garbage disposal Algorithm design and analysis multi-robot systems Immune System Heuristic algorithms ant algorithm control systems Mobile robots ADMINISTRATIVE CONSENT ORDER Robot robotics Recycling Graph representations

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A Resampling ant colony optimization with Elite Exploration and Convergence Mechanism for Multithreshold Segmentation of Breast Cancer Images

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2024年第9期6卷

作者： Wang, Zhen Zhao, Dong Heidari, Ali Asghar Chen, Yi Chen, Huiling Liang, Guoxi Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran 999067 Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details. RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local

关键词： ant colony optimization algorithms breast cancers metaheuristic algorithms threshold image segmentations

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Hybrid Approaches based on Simulated Annealing, Tabu Search and ant colony optimization for Solving the k-Minimum Spanning Tree Problem

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第2期12卷 708-712页

作者： Addou, El Houcine Serghini, Abelhafid Mermri, El Bekkaye Mohammed I Univ LANO Lab FSO ESTO BV Mohamed VI BP 717 Oujda 60000 Morocco Mohammed I Univ FSO BV Mohamed VI BP 717 Oujda 60000 Morocco

In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the combination of simulated annealing, tabu search and ant colony optimization algorithms. The performances of the proposed methods are compared to other approaches from the literature using the same well-known library of benchmark instances.

关键词： k-Minimum spanning tree metaheuristics simulated annealing ant colony optimization algorithms tabu search approximation algorithms

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Dynamic construction site layout planning using max-min ant system

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AUTOMATION IN CONSTRUCTION 2010年第1期19卷 55-65页

作者： Ning, Xin Lam, Ka-Chi Lam, Mike Chun-Kit City Univ Hong Kong Dept Bldg & Construct Hong Kong Hong Kong Peoples R China Dongbei Univ Finance & Econ Sch Investment & Construct Management Dalian Liaoning Prov Peoples R China

Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant system (MMAS) algorithm, which is one of the ant colony optimization (ACO) algorithms, to solve the dynamic CSLP problem under the two congruent objective functions of minimizing safety concerns and reducing construction cost is proposed. Using weighted sum method the MOO problem can be solved by the proposed MMAS method. An office building case was used to verify the capability of the proposed method to solve dynamic CSLP problem and the results are promising. The approach could be benchmarked by researchers using other advanced optimization algorithms to solve the same problem or expand the applications to other fields. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Dynamic construction site layout planning Max-min ant system ant colony optimization algorithms Multi-objective optimization Continuous dynamic searching scheme

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A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities

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APPLIED SOFT COMPUTING 2018年 69卷 14-32页

作者： Birjali, Marouane Beni-Hssane, Abderrahim Erritali, Mohammed Univ Chouaib Doukkali Dept Comp Sci LAROSERI Lab Fac Sci El Jadida Morocco Univ Sultan Moulay Slimane Fac Sci & Technol Dept Comp Sci TIAD Lab Beni Mellal Morocco

The e-learning paradigm is becoming one of the most important educational methods, which is a decisive factor for learning and for making learning relevant. However, most existing e-learning platforms offer traditional e-learning system in order that learners access the same evaluation and learning content. In response, Big Data technology in the proposed adaptive e-learning model allowed to consider new approaches and new learning strategies. In this paper, we propose an adaptive e-learning model for providing the most suitable learning content for each learner. This model based on two levels of adaptive e-learning. The first level involves two steps: (1) determining the relevant future educational objectives through the adequate learner e-assessment method using MapReduce-based Genetic Algorithm, (2) generating adaptive learning path for each learner using the MapReduce-based ant colony optimization algorithm. In the second level, we propose MapReduce-based Social Networks Analysis for determining the learner motivation and social productivity in order to assign a specific learning rhythm to each learner. Finally, the experimental results show that the presented algorithms implemented on Big Data environment converge much better than those implementations with traditional concurrent works. Also, this work provides main benefit because it describes how Big Data technology transforms e-learning paradigm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Adaptive e-learning Big data MapReduce Genetic algorithm Personalized learning path ant colony optimization algorithms Social networks analysis Motivation and productivity Learning content

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