Designing A Shorter Form of the Big Three Perfectionism Scale: An Application of Ant Colony Optimization

2021 ◽  
pp. 073428292110558
Author(s):  
Sevilay Kilmen
Keyword(s):  
Psychometric Properties ◽  
Ant Colony Optimization ◽  
Short Form ◽  
Optimization Procedure ◽  
Educational Assessment ◽  
Ant Colony ◽  
Short Versions

The present study has two main purposes. The first is to create a short form of the BTPS and to evaluate the psychometric properties of the short form. The second is to evaluate the performance of the ant colony optimization procedure and discuss the applicability of the ant colony optimization procedure in creating a short form. Results revealed that the 30-item short form of the BTPS can be applied to psychological or educational assessment settings to obtain valid and reliable results related to ten different facets of perfectionism. The current study also showed that the ant colony optimization procedure can be used to create the best short form which has variance, reliability, and high factor correlations between original and short versions of a scale.

scholarly journals The HEX-ACO-18: Developing an age-invariant HEXACO short scale using Ant Colony Optimization

2021 ◽  
Author(s):  
Gabriel Olaru ◽  
Kristin Jankowsky
Keyword(s):  
Ant Colony Optimization ◽  
Measurement Invariance ◽  
Short Form ◽  
Ant Colony ◽  
Model Fit ◽  
Equation Modeling ◽  
Model Estimation ◽  
Adequate Model ◽  
Short Scale ◽  
Sampling Procedures

In this study, we developed an age-invariant 18-item short form of the HEXACO Personality Inventory for use in developmental personality research. We employed a combination of the item selection procedure ant colony optimization (ACO) and the model estimation approach local structural equation modeling (LSEM). ACO is a metaheuristic algorithm that selects and evaluates items based on the quality of the resulting short scale, thus allowing for the direct optimization of criteria that can only be estimated based on combinations of items, such as model fit and measurement invariance across age. LSEM allows for model estimation and measurement invariance testing across a continuous age variable by weighting participants based on their age, rather than splitting the sample into artificial age groups. Using a HEXACO-100 dataset of N = 6,419 participants ranging from 16 to 90 years of age, we selected a short form optimized for model fit, measurement invariance, facet coverage, and balance of item keying. The resulting HEX ACO 18 short scale showed adequate model fit, scalar measurement invariance across age, and covered three out of four facets from each HEXACO trait domain. Furthermore, the usefulness and versatility of the item and person sampling procedures ACO and LSEM is demonstrated.

Optimization of Heliostat Aim Point Selection for Central Receiver Systems Based on the Ant Colony Optimization Metaheuristic

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Boris Belhomme ◽  
Robert Pitz-Paal ◽  
Peter Schwarzbözl
Keyword(s):  
Ant Colony Optimization ◽  
Electrical Power ◽  
Optimization Procedure ◽  
Ant Colony ◽  
Test Case ◽  
Point Selection ◽  
Central Receiver ◽  
Universal Procedure ◽  
Solar Power Tower ◽  
Selection Of

The optimization of the selection of heliostat aim points in a solar power tower plant with the objective of an increased overall efficiency represents a NP-hard optimization problem of high dimension. This paper presents a universal procedure for the purpose of aim point optimization based on the ant colony optimization metaheuristic that uses the principles of swarm intelligence. The applicability of the developed aim point optimization procedure to central receiver systems is demonstrated on a test case, for which the electrical power of a concentrated photovoltaic (CPV) receiver is maximized for a selected operating point. The example of a CPV receiver was chosen due to its nonlinear and nonmonotonous dependency of efficiency and flux density. It is shown that the optimization result is very close to the theoretical maximum.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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