Developing a new suit sizing system using data optimization techniques

2012 ◽  
Vol 24 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Maryam Salehi Esfandarani ◽  
Jamal Shahrabi
Keyword(s):  
Optimization Techniques ◽  
Sizing System ◽  
Using Data ◽  
Data Optimization

Algorithm and Data Optimization Techniques for Scaling to Massively Threaded Systems

Computer ◽  
2012 ◽  
Vol 45 (8) ◽  
pp. 26-32 ◽  
Author(s):  
John A. Stratton ◽  
Christopher Rodrigues ◽  
I-Jui Sung ◽  
Li-Wen Chang ◽  
Nasser Anssari ◽  
...  
Keyword(s):  
Optimization Techniques ◽  
Data Optimization

scholarly journals Data Optimization Techniques using Bloom Filter in Big Data

2016 ◽  
Vol 142 (3) ◽  
pp. 23-27
Author(s):  
Ritu Jain ◽  
Mukesh Rawat ◽  
Swati Jain
Keyword(s):  
Big Data ◽  
Bloom Filter ◽  
Optimization Techniques ◽  
Data Optimization

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

Scattered Data Interpolation Using Data Dependant Optimization Techniques

2002 ◽  
Vol 64 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Günther Greiner ◽  
Andreas Kolb ◽  
Angela Riepl
Keyword(s):  
Scattered Data ◽  
Optimization Techniques ◽  
Data Interpolation ◽  
Scattered Data Interpolation ◽  
Using Data

Optimum allocation by dynamic programming for sampling on successive occasions with partial replacement of units

1982 ◽  
Vol 12 (2) ◽  
pp. 264-269 ◽  
Author(s):  
Stephen A.Y. Omule ◽  
Douglas H. Williams
Keyword(s):  
Dynamic Programming ◽  
Design Problem ◽  
Decision Model ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Optimum Allocation ◽  
Partial Replacement ◽  
Using Data ◽  
The Cost ◽  
Sequential Nature

The sequential nature of sampling on successive occasions with partial replacement of units (SPR) was exploited to cast the optimal SPR design problem as a multistage decision model which could be optimized through dynamic programming (DP). The objective of the design was to determine the number of remeasured and new sampling units at the successive occasions such that the cost of sampling was minimized and subject to the side conditions that the specified variance levels of the variables of interest were met. Using data published elsewhere, the DP approach was shown to give solutions almost identical to those obtained by other optimization techniques such as the graphical procedure. The advantage of the DP approach, however, was that it could handle optimization problems in which the sampling extended over several occasions.

scholarly journals Big Data Optimization Techniques: A Survey

2018 ◽  
Vol 10 (4) ◽  
pp. 41-48 ◽  
Author(s):  
Chandrima Roy ◽  
◽  
Siddharth Swarup Rautaray ◽  
Manjusha Pandey
Keyword(s):  
Big Data ◽  
Optimization Techniques ◽  
Data Optimization

scholarly journals Decision support system for location selection of convenience stores and retail facilities using optimization techniques and GIS

2021 ◽  
Vol 2134 (1) ◽  
pp. 012015
Author(s):  
O K Golovnin ◽  
A A Igonina
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Spatial Location ◽  
Optimal Location ◽  
Optimization Techniques ◽  
Location Selection ◽  
Convenience Stores ◽  
Using Data ◽  
Selection Of

Abstract The successful spatial location of small convenience stores and retail facilities greatly affects the potential flow of customers, therefore the task of optimizing their location is primary when opening a new store or revising an existing stores’ location. We developed a decision support system for location selection of convenience stores and retail facilities using optimization techniques and a geographic information system. The system identifies areas on an electronic map with the greatest potential to locate a store or retail facility, taking into account its specifics, pedestrian accessibility, opening hours, and a set of goods or services. The system calculates the optimal location of one or several stores using one of the optimization techniques for the criterion of the number of potential customers. In our experiments, we used gradient descent for optimization. The experiments were carried out in areas of various sizes using data obtained from Open Street Map. Experimental results showed that the system finds the optimal location in a reasonable time and therefore it can be useful for small convenience stores and retail facilities.

Household annuitization decisions: simulations and empirical analyses

2004 ◽  
Vol 3 (2) ◽  
pp. 109-143 ◽  
Author(s):  
IRENA DUSHI ◽  
ANTHONY WEBB
Keyword(s):  
Numerical Optimization ◽  
Oldest Old ◽  
Household Wealth ◽  
Optimization Techniques ◽  
Annuity Market ◽  
Health Dynamics ◽  
Increasing Demand ◽  
Using Data ◽  
Retirement Study ◽  
Made In

Annuities provide insurance against outliving one's wealth. Previous studies have indicated that, for many households, the value of the longevity insurance should outweigh the actuarial unfairness of prices in the voluntary annuity market. Nonetheless, voluntary annuitization rates are extremely low.Previous research on the value of annuitization has compared an optimal decumulation of unannuitized wealth with the alternative of annuitizing all unannuitized wealth at age 65. We relax these assumptions, allowing households to annuitize any part of their unannuitized wealth at any age and to return to the annuity market as many times as they wish.Using numerical optimization techniques, assuming the levels of actuarial unfairness of annuities calculated in previous research, and retaining the assumption made in previous research that one half of household wealth is pre-annuitized, we conclude that it is optimal for couples to delay annuitization until they are aged 73–82, and in some cases never to annuitize. It is usually optimal for single men and women to annuitize at substantially younger ages, between 65 and 70. Households that annuitize will generally wish to annuitize only part of their unannuitized wealth.Using data from the Asset and Health Dynamics Among the Oldest Old and Health and Retirement Study panels, we show that much of the failure of the average currently retired household to annuitize can be attributed to the exceptionally high proportions of the wealth of these cohorts that is pre-annuitized. We expect younger cohorts to have smaller proportions of pre-annuitized wealth and project increasing demand for annuitization as successive cohorts age.

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