1,151 research outputs found

    Analysis of Independent Roulette Selection in Parallel Ant Colony Optimization

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    The increased availability of high-performance parallel architectures such as the Graphics Processing Unit (GPU) has led to significant interest in modified versions of metaheuristics that take advantage of their capabilities. Parallel Ant Colony Optimization (ACO) algorithms are now widely-used, but these often present a challenge in terms of maximizing the potential for parallelism. One common bottleneck for parallelization of ACO occurs during the tour construction phase, when edges are probabilistically selected. Independent Roulette (I-Roulette) is an alternative to the standard Roulette Selection method used during this phase, and this achieves significant performance improvements on the GPU. In this paper we provide the first in-depth study of how I-Roulette works. We establish that, even though I-Roulette works in a qualitatively different way to Roulette Wheel selection, its use in two popular ACO variants does not affect the quality of the solutions obtained. However, I-Roulette significantly accelerates convergence to a solution. Our theoretical analysis shows that I-Roulette possesses several interesting and non-obvious features, and is capable of a form of dynamical adaptation during the tour construction process

    Re-engineering the ant colony optimization for CMP architectures

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    [EN] The ant colony optimization (ACO) is inspired by the behavior of real ants, and as a bioinspired method, its underlying computation is massively parallel by definition. This paper shows re-engineering strategies to migrate the ACO algorithm applied to the Traveling Salesman Problem to modern Intel-based multi- and many-core architectures in a step-by-step methodology. The paper provides detailed guidelines on how to optimize the algorithm for the intra-node (thread and vector) parallelization, showing the performance scalability along with the number of cores on different Intel architectures, reporting up to 5.5x speedup factor between the Intel Xeon Phi Knights Landing and Intel Xeon v2. Moreover, parallel efficiency is provided for all targeted architectures, finding that core load imbalance, memory bandwidth limitations, and NUMA effects on data placement are some of the key factors limiting performance. Finally, a distributed implementation is also presented, reaching up to 2.96x speedup factor when running the code on 3 nodes over the single-node counterpart version. In the latter case, the parallel efficiency is affected by the synchronization frequency, which also affects the quality of the solution found by the distributed implementation.This work was partially supported by the Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities as well as European Commission FEDER funds under Grants TIN2015-66972-C5-3-R, RTI2018-098156-B-C53, TIN2016-78799-P (AEI/FEDER, UE), and RTC-2017-6389-5. We acknowledge the excellent work done by Victor Montesinos while he was doing a research internship supported by the University of Murcia.Cecilia-Canales, JM.; García Carrasco, JM. (2020). Re-engineering the ant colony optimization for CMP architectures. The Journal of Supercomputing (Online). 76(6):4581-4602. https://doi.org/10.1007/s11227-019-02869-8S45814602766Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, LebanonAkila M, Anusha P, Sindhu M, Selvan Krishnasamy T (2017) Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas. In: IEEE Applied Electromagnetics Conference (AEMC)Dorigo M, Stützle T (2004) Ant colony optimization. A bradford book. The MIT Press, CambridgeCecilia JM, García JM, Nisbet A, Amos M, Ujaldón M (2013) Enhancing data parallelism for ant colony optimization on GPUs. J Parallel Distrib Comput 73(1):42–51Dawson L, Stewart I (2013) Improving ant colony optimization performance on the GPU using CUDA. In: IEEE Conference on Evolutionary Computation, pp 1901–1908Llanes A, Cecilia JM, Sánchez A, García JM, Amos M, Ujaldón M (2016) Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Comput 19(1):1–11Cecilia JM, Llanes A, Abellán JL, Gómez-Luna J, Chang L, Hwu WW (2018) High-throughput ant colony optimization on graphics processing units. J Parallel Distrib Comput 113:261–274Lloyd H, Amos M (2016) A Highly Parallelized and Vectorized Implementation of Max–Min Ant System on Intel Xeon Phi. In: IEEE computational intelligenceTirado F, Barrientos RJ, González P, Mora M (2017) Efficient exploitation of the Xeon Phi architecture for the ant colony optimization (ACO) metaheuristic. J Supercomput 73(11):5053–5070Montesinos V, García JM (2018) Vectorization strategies for ant colony optimization on intel architectures. Parallel Computing is Everywhere. IOS Press, Amsterdam, pp 400–409Lawler E, Lenstra J, Kan A, Shmoys D (1987) The Traveling salesman problem. Wiley, New YorkMontesinos V (June 2018) Performance analysis of ant colony optimization on intel architectures. Master’s Thesis, University of Murcia (Spain)Lloyd H, Amos M (2017) Analysis of independent roulette selection in parallel ant colony optimization. In: Genetic and Evolutionary Computation Conference, ACM, pp 19–26Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano, ItalyDuran A, Klemm M (2012) The intel many integrated core architecture. In: Internal Conference on High Performance Computing and Simulation (HPCS), pp 365–366The OpenMP API specification for parallel programming. URL: https://www.openmp.org . [Last accessed 14 June 2018]The Message Passing Interface (MPI) standard. URL: http://www.mcs.anl.gov/research/projects/mpi/ . [Last accessed 15 June 2018]Vladimirov A, Asai R (2016) Clustering modes in Knights landing processors: developer’s guide. Colfax international. URL: https://colfaxresearch.com/knl-numa/ . [Last accessed: 16 June 2018]Intel Developer Zone. URL: https://software.intel.com/en-us/modern-code . 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Future Gener Comput Syst 79:473–487Peake J, Amos M, Yiapanis P, Lloyd H (2018) Vectorized candidate set selection for parallel ant colony optimization. In: Genetic and Evolutionary Computation Conference, ACM, pp 1300–1306Stützle T (1998) Parallelization strategies for ant colony optimization. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel problem solving from nature—PPSN V. PPSN. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, HeidelbergAbdelkafi O, Lepagnot J, Idoumghar L (2014) Multi-level parallelization for hybrid ACO. In: Siarry P, Idoumghar L, Lepagnot J (eds) Swarm Intelligence Based Optimization. ICSIBO 2014. Lecture Notes in Computer Science, vol 8472. Springer, ChamMichel R, Middendorf M (1998) An island model based ant system with lookahead for the shortest super sequence problem. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel problem solving from nature— PPSN V. PPSN. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, HeidelbergChen L, Sun H, Wang S (2008) Parallel implementation of ant colony optimization on MPP. In: International Conference on Machine Learning and CyberneticsLin Y, Cai H, Xiao J, Zhang J (2007) Pseudo parallel ant colony optimization for continuous functions. In: International Conference on Natural Computatio

    Orthogonal methods based ant colony search for solving continuous optimization problems

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    Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

    Generic Techniques in General Purpose GPU Programming with Applications to Ant Colony and Image Processing Algorithms

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    In 2006 NVIDIA introduced a new unified GPU architecture facilitating general-purpose computation on the GPU. The following year NVIDIA introduced CUDA, a parallel programming architecture for developing general purpose applications for direct execution on the new unified GPU. CUDA exposes the GPU's massively parallel architecture of the GPU so that parallel code can be written to execute much faster than its sequential counterpart. Although CUDA abstracts the underlying architecture, fully utilising and scheduling the GPU is non-trivial and has given rise to a new active area of research. Due to the inherent complexities pertaining to GPU development, in this thesis we explore and find efficient parallel mappings of existing and new parallel algorithms on the GPU using NVIDIA CUDA. We place particular emphasis on metaheuristics, image processing and designing reusable techniques and mappings that can be applied to other problems and domains. We begin by focusing on Ant Colony Optimisation (ACO), a nature inspired heuristic approach for solving optimisation problems. We present a versatile improved data-parallel approach for solving the Travelling Salesman Problem using ACO resulting in significant speedups. By extending our initial work, we show how existing mappings of ACO on the GPU are unable to compete against their sequential counterpart when common CPU optimisation strategies are employed and detail three distinct candidate set parallelisation strategies for execution on the GPU. By further extending our data-parallel approach we present the first implementation of an ACO-based edge detection algorithm on the GPU to reduce the execution time and improve the viability of ACO-based edge detection. We finish by presenting a new color edge detection technique using the volume of a pixel in the HSI color space along with a parallel GPU implementation that is able to withstand greater levels of noise than existing algorithms

    Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

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    Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

    An Area-Optimized Chip of Ant Colony Algorithm Design in Hardware Platform Using the Address-Based Method

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    The ant colony algorithm is a nature-inspired algorithm highly used for solving many complex problems and finding optimal solutions; however, the algorithm has a major flaw and that is the vast amount of calculations and if the proper correction algorithm and architectural design are not provided, it will lead to the increasing use of hardware platform due to the high volume of operations; and perhaps at higher scales, it causes the chip area not to work because of the high number of problems; hence, the purpose of this paper is to save the hardware platform as far as possible and use it optimally through providing a particular algorithm running on a reconfigurable chip driven by the address-based method, so that the comparison of synthesis operations with the similar works shows significant improvements as much as 1/3 times greater than the other similar hardware methods.DOI:http://dx.doi.org/10.11591/ijece.v4i6.692

    Hybridization of Biologically Inspired Algorithms for Discrete Optimisation Problems

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    In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed
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