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A speculative approach to parallelization in particle swarm optimization

Matthew Gardner, Andrew McNabb, Kevin Seppi
2011 Swarm Intelligence  
Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors.  ...  Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm.  ...  Particle Swarm Optimization Particle swarm optimization was proposed in 1995 by James Kennedy and Russell Eberhart [Kennedy and Eberhart, 1995] .  ... 
doi:10.1007/s11721-011-0066-8 fatcat:oludvepxr5gm3eityh5t4zrpxi

Optimal Multilevel Thresholds based on Tsallis Entropy Method using Golden Ratio Particle Swarm Optimization for Improved Image Segmentation

K Manikantan, Arun B V, Darshan Kumar S Yaradoni
2012 Procedia Engineering  
This paper proposes a novel method of Particle Swarm Optimization, called Golden Ratio Particle Swarm Optimization (GRPSO), based on Golden Ratio found in nature.  ...  Improved performance of GRPSO in image segmentation is established by comparison of objective values achieved by GRPSO with those achieved by Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and  ...  Golden Ratio Particle Swarm Optimization The proposed Golden Ratio Particle Swarm Optimization (GRPSO) is based on Golden Ratio (GR) [17] found in nature, given by (1 5) / 2 1.6180339887...  ... 
doi:10.1016/j.proeng.2012.01.873 fatcat:34l3cuxdgzcx3jewiytlvpp6g4

Parallel global optimization with the particle swarm algorithm

J. F. Schutte, J. A. Reinbolt, B. J. Fregly, R. T. Haftka, A. D. George
2004 International Journal for Numerical Methods in Engineering  
To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (  ...  Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor.  ...  Evaluate objective function for particle It is speculated that because the updating occurs immediately after each fitness evaluation, the swarm reacts more quickly to an improvement in the best-found fitness  ... 
doi:10.1002/nme.1149 pmid:17891226 pmcid:PMC1989676 fatcat:tz6qlmiyizdjvm6mxb5smxi5iu

Cooperative Area Extension of PSO - Transfer Learning vs. Uncertainty in a Simulated Swarm Robotics

Adham Atyabi, David M. W. Powers
2013 Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics  
The study investigates the effectiveness of 2 variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in simulated robotic environments affected  ...  The results highlight the feasibility of CAEPSO to be used as the controller and decision maker of a swarm of robots in the simulated uncertain environment when gained expertise from past training is transferred  ...  PARTICLE SWARM OPTIMIZATION (PSO) Basic PSO Basic PSO is an evolutionary approach introduced by Kennedy and Eberhart in 1995 and it is inspired from animal social behaviors.  ... 
doi:10.5220/0004456901770184 dblp:conf/icinco/AtyabiP13 fatcat:tqy7ubys65a77c5venup6kzp6q

Particle Swarms: The Second Decade

Riccardo Poli, Jim Kennedy, Tim Blackwell, Alex Freitas
2008 Journal of Artificial Evolution and Applications  
Finally, we would like to thank EPSRC (Extended Particle Swarms project, GR/T11234/01) for financial support. Riccardo Poli Jim Kennedy Tim Blackwell Alex Freitas  ...  ACKNOWLEDGMENTS We would like to thank the Editor-in-Chief, Stefano Cagnoni, for his support in putting together this special issue.  ...  In the article "An improved particle swarm optimizer for placement constraints," S. In "Particle swarm optimization for antenna designs in engineering electromagnetics," N. Jin and Y.  ... 
doi:10.1155/2008/108972 fatcat:7f3uhnr2ufgbxcblo5vjnyru7q

Particle swarm guided evolution strategy

Chang-Tai Hsieh, Chih-Ming Chen, Ying-ping Chen
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
Evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics for tackling real-parameter optimization problems in evolutionary computation.  ...  , called particle swarm guided evolution strategy.  ...  Particle Swarm Optimization PSO is a population-based stochastic optimization technique developed in 1995, inspired by the social behavior of bird flocking or fish schooling.  ... 
doi:10.1145/1276958.1277096 dblp:conf/gecco/HsiehCC07 fatcat:uym7m53lmnak5jjquizeaus3bu

Overlapping swarm intelligence for training artificial neural networks

Karthik Ganesan Pillai, John W. Sheppard
2011 2011 IEEE Symposium on Swarm Intelligence  
Gradient based methods might not always lead to a globally optimal solution of the network.  ...  A novel overlapping swarm intelligence algorithm is introduced to train the weights of an artificial neural network.  ...  Particle Swarm Optimization: Particle Swarm Optimization (PSO) proposed by Eberhart and Kennedy [9] , is a technique inspired by the social behavior of flocking birds.  ... 
doi:10.1109/sis.2011.5952566 dblp:conf/swis/PillaiS11 fatcat:jrsrec3dgfe3zfm574ze3noyge

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Lei Yang, Yu Dai, Bin Zhang
2017 Mathematical Problems in Engineering  
The experiments show that the proposed framework has better performance in the virtual cluster compared with the current speculative execution framework.  ...  The core of the framework is to identify the straggler tasks in a job and back up these tasks to make the backed up one overtake the original tasks in order to reduce the overall response time of the job  ...  Acknowledgments This work was supported in part by the National Key Technology R&D Program of the Ministry of Science and Technology (2015BAH09F02 and 2015BAH47F03), National Natural Science Foundation  ... 
doi:10.1155/2017/2724531 fatcat:6kfxvjzz5rd7vekkzlhnzlff5a

Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies

Peng-Yeng Yin, Fred Glover, Manuel Laguna, Jia-Xian Zhu
2010 European Journal of Operational Research  
Particle swarm optimization (PSO) has emerged as an acclaimed approach for solving complex optimization problems.  ...  in solutions that lie on paths from good solutions to other good solutions.  ...  Introduction The particle swarm optimization (PSO) algorithm, introduced by Kennedy and Eberhart (1995) , simulates a model of sociocognition.  ... 
doi:10.1016/j.ejor.2009.03.035 fatcat:sxx3wcnjwjbylj6vujlxps56ma

A Hybrid Forecasting Model for Option Price Prediction using Machine Learning Technique

For forecasting the option price, the methods which are used in this paper are SOM (self organization map), RBF (Radial Basis Function) and the Hybrid Swarm Optimization system.  ...  A new move is proposed by this paper that is forecasting the option price with the use of ANN model which is optimizing the hybrid swarm optimization.  ...  (Particle swarm Optimization) as well as BCO (Bee Colony Optimization).  ... 
doi:10.35940/ijitee.a4622.119119 fatcat:igtzg5xoz5aqli5i76y2pxabjq

Hybrid particle swarm optimization: Evolutionary programming approach for solving generation maintenance scheduling problem

Giftson Samuel G, Christober Asir Rajan C
2013 Scientific Research and Essays  
This paper presents a hybrid particle swarm optimization based genetic algorithm and hybrid particle swarm optimization based evolutionary programming for solving long-term generation maintenance scheduling  ...  While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint.  ...  PARTICLE SWARM OPTIMIZATION Particle swarm optimization (PSO) is inspired from the collective behavior exhibited in swarms of social insects.  ... 
doi:10.5897/sre2013.5612 fatcat:kxrnwoued5cmrbvh7cq6flsxtm

Feature Weighting Improvement of Web Text Categorization Based on Particle Swarm Optimization Algorithm

Yonghe Lu, Yanhong Peng
2015 Journal of Computers  
In the feature tag weighting algorithm, we use particle swarm optimization (PSO) to calculate tag weighting coefficients; lastly, k-nearestneighbor (kNN) is used as the web text categorization.  ...  To solve this problem, a new feature weighting algorithm based on Particle Swarm Optimization algorithm is put forward. It considers the structure information (i.e., HTML tags) of web pages.  ...  Particle Swarm Optimization In Particle Swarm Optimization, each particle represents a possible solution to the optimization task.  ... 
doi:10.17706/jcp.10.4.260-267 fatcat:lbtw6ekwtfh2xojakr73thtsge

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions

Jia Guo, Yuji Sato
2017 International Journal of Networked and Distributed Computing (IJNDC)  
Bare bones particle swarm optimization is a parameter-free swarm intelligence algorithm which is famous for easy applying.  ...  Hence, a pair-wise bare bones particle swarm optimization algorithm is proposed in this paper to balance the exploration and exploitation.  ...  Related work Particle swarm optimization Particles in particle swarm optimization (PSO) is an abstract conception. It is proposed for nonlinear functions.  ... 
doi:10.2991/ijndc.2017.5.3.3 fatcat:nswhxg2g25flvis5nzvu2ign7a

Comparative Analysis of Various Evolutionary Techniques of Load Balancing: A Review

Manvi Mishra, Shivali Agarwal, Payal Mishra, Shalini Singh
2013 International Journal of Computer Applications  
They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with complex problems.  ...  For a decade swarm Intelligence deals with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies.  ...  Max-Min Particle Swarm Optimization Max-Min particle swarm optimization (Max-Min PSO) is based on the task scheduling in grid environment.  ... 
doi:10.5120/10540-4675 fatcat:rw4rsjnj7ngmritbik27cyun7u

ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data [article]

David Vadnais, Michael Middleton, Oluwatosin Oluwadare
2021 bioRxiv   pre-print
Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization approach called ParticleChromo3D.  ...  We evaluated our algorithm on simulated and real Hi-C data in this work. Our results show that ParticleChromo3D is more accurate than most of the existing algorithms for 3D structure reconstruction.  ...  ParticleChromo3D uses Particle Swarm Optimization (PSO) to generate 3D structures of chromosomes from Hi-C data.  ... 
doi:10.1101/2021.02.11.430871 fatcat:y3k6n5mbbvbt5ocncgpfsbpnl4
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