PSOt - a particle swarm optimization toolbox for use with Matlab

Birge, Brian

doi:10.1109/sis.2003.1202265

Cited by 186 publications

(127 citation statements)

References 2 publications

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“…T-ACO was tested against two implementation of Genetic Algorithms, the MATLAB Genetic Algorithm and Direct Search Toolbox (GATBX) [15] and NSGA-II [16], and an implementation of Particle Swarm Optimization (PSOt) [17]. Settings for the typical parameters of each one of the optimizers will be specified for each test case.…”

Section: Case Studiesmentioning

confidence: 99%

“…The number of generations in GA algorithms is denoted by Generations and the number of iteration in PSOt by the parameter iter. For the specific meaning of the parameters StallGenLimit, pcross bin, pmut bin, iiw and fiw, please refer respectively to [15], [16], and [17]. Due to the stochastic nature of the heuristics used in the tests, all the algorithms were run for 200 times.…”

Section: Case Studiesmentioning

confidence: 99%

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An Ant System algorithm for automated trajectory planning

Ceriotti

Vasile

2010

IEEE Congress on Evolutionary Computation

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Abstract-The paper presents an Ant System based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision one all the previouslymade decisions. In the case of multi-gravity assist trajectories planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solution incrementally, according to Ant System paradigms. Unlike standard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms.

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Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

An Ant System algorithm for automated trajectory planning

Ceriotti

Vasile

2010

IEEE Congress on Evolutionary Computation

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“…4.4 trends, and cycles and anomalies can be detected by means of a de-noising process (Lewicki et al, 2005). Figure 2 shows simulations of net CO 2 flux performed with the ISOLSM model (Riley et al, 2002;2003) for the typical conditions of the Morgan-Monroe State Forest, Indiana, USA (Ehman et al, 2002). The simulations reproduce the very large diurnal variations in the net CO 2 flux that are typically observed in field conditions, and the more regular night-time daily evolution (in red) of respiration.…”

Section: Spatial Supportmentioning

confidence: 99%

“…Novelty detection deals with the identification of new or unknown data that a machine-learning system is not aware of during training (Markou and Singh, 2003a;b). Novelty detection treats anomalies in two ways, namely stochastically (e.g., parametric and non-parametric tests) and deterministically (e.g., neural network classifiers).…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

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The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Cortis

Oldenburg

Benson

2008

International Journal of Greenhouse Gas Control

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Storage of large amounts of carbon dioxide (CO 2 ) in deep geological formations for greenhousegas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this work we explore various optimization strategies for characterizing surface leakage (seepage) using near-surface measurement approaches such as accumulation chambers and eddy covariance towers. Seepage characterization objectives and limitations need to be defined carefully from the outset especially in light of large natural background variations that can mask seepage. The cost and sensitivity of seepage detection are related to four critical length scales pertaining to the size of the: (1) region that needs to be monitored; (2) footprint of the measurement approach, and (3) main seepage zone; and (4) region in which concentrations or fluxes are influenced by seepage. Seepage characterization objectives may include one or all of the tasks of detecting, locating, and quantifying seepage. Each of these tasks has its own optimal strategy. Detecting and locating seepage in a region in which there is no expected or preferred location for seepage nor existing evidence for seepage requires monitoring on a fixed grid, e.g., using eddy covariance towers. The fixed-grid approaches needed to detect seepage are expected to require large numbers of eddy covariance towers for large-scale geologic CO 2 storage. Once seepage has been detected and roughly located, seepage zones and features can be optimally pinpointed through a dynamic search strategy, e.g., employing accumulation chambers and/or soil-gas sampling. Quantification of seepage rates can be done through measurements on a localized fixed grid once the seepage is pinpointed. Background measurements are essential for seepage detection in natural ecosystems. Artificial neural networks are considered as regression models useful for distinguishing natural system behavior from anomalous behavior suggestive of CO 2 seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO 2 fluxes and concentrations in

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Nature‐inspired soft computing for financial option pricing using high‐performance analytics

Thulasiram

Thulasiraman

Prasain

et al. 2014

Concurrency and Computation

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SUMMARYHigh-performance computing has witnessed the push towards computer hardware design in the past decade. Many real world problems are both data and compute intensive. Designing efficient algorithms is important to make effective use of the hardware resources for fast data analysis. Finance is one application that will benefit from these supercomputers. Options are instruments that give opportunity to profit from market movements without making large investments. However, understanding the asset price behavior and making a decision to enter into an option contract is quite challenging, called option pricing problem, because underlying asset price might vary violently. In this paper, we propose a nature-inspired soft computing, meta-heuristic, particle swarm optimization (PSO) algorithm to price options. We modify the PSO algorithm and incorporate varying volatility parameters to price options. The proposed algorithm, PSO with Varying Volatility (PSOwVV), is experimented with various PSO and financial parametric conditions. We also develop a parallel PSOwVV algorithm and implement on a distributed shared memory multi-core machine. We show that the parallel algorithm performs well when the number of particles is linearly proportional to the number of processors. The parallel algorithm achieves a speedup of approximately 20 with 64 particles on a four node hybrid cluster.

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PSOt - a particle swarm optimization toolbox for use with Matlab

Abstract: ~ A Particle Swarm Optimization Toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with some examples. A link to downloadable code is provided.

Cited by 186 publications

References 2 publications

An Ant System algorithm for automated trajectory planning

An Ant System algorithm for automated trajectory planning

The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Nature‐inspired soft computing for financial option pricing using high‐performance analytics

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