An Improved Ant Colony Algorithm for the Optimization of Skeletal Structures by the Proposed Sampling Search Space Method

Designing space is dramatically enlarged with optimization of structures based on ACO, regard to increasing section’s list. This problem decreases the speed of optimization in order to reach to optimum point and also increases local optimum probability, because determining suitable cross section process for each design variable in ACO depends on number of members in the list of section. Therefore, this paper by using partitioning the design space tries to decrease the probability of achieving local optimum during the process of structures optimum design by ACO and to increase the speed of convergence. In this regard, the list of section is divided to specific number of subsets inspired by meshing process in finite element. Then a member of each subset (in three case, maximum, middle and minimum of cross section) is defined as a representative of subset in a new list. Optimization process starts based on the new lit of section (global search). After specific number of repetitions, optimum design range for each variable will be determined. Afterward, variable section list is defined for each design variable related to result of previous step of process and based on subset of related variable. Finally, optimization process is continued based on the new list of section for each design variable to the end of process (local search). Proposal is studied in three cases and compared with common method in ACO and standard optimization examples in skeletal structures are used. Results show an increase in accuracy and speed of optimization according to cross section middle method (Case 2)

Clustering Elements of Truss Structures for Damage Identification by CBO

The number of structural elements plays a significant role in detecting damage location and severity; such methods have sometimes failed to provide correct solutions due to the entrapment of damage detection algorithms in the local optimum. To resolve this problem, this study proposed the simultaneous use of mathematical and statistical methods to narrow down the search space. To this end, a two-step damage detection method was proposed. In the first step, the structural elements were initially divided into different clusters using the k-means method. Subsequently, the possibly damaged elements of each cluster were identified. In the second step, the elements selected in the first step were placed in a new set, and a process was applied to identify their respective damage location and severity. Thus, the proposed method reduced the search space as well as the possibility of entrapment in the local optimum. Other advantages of the proposed method include the use of fewer dynamic properties. Accordingly, by narrowing down the search space and the dimensions of the system for governing equations, the proposed method could significantly increase the chance of obtaining favorable results in structures with many elements and those with few vibration modes. A meta-heuristic method, called the colliding bodies optimization (CBO), was used in the proposed damage detection optimization algorithm. The optimization problem was based on the modal strain energy equations. According to the results, the proposed method was able to detect the location and severity of damage, even at its slightest percentage

Optimal Design of Steel Towers Using a Multi-Metaheuristic Based Search Method

In meta-heuristic algorithms, the problem of parameter tuning is one of the most important issues that can be highly time consuming. To overcome this difficulty, a number of researchers have improved the performance of their methods by enhancement and hybridization with other algorithms. In the present paper efforts are made to search design space simultaneously by the Multi Metaheuristic based Search Method (MMSM). In the proposed method, optimization process is performed by dividing the initial population into five subsets so-called islands. An improved multi-metaheuristic method is then employed. After a certain number of repetitions (migration intervals), some percent of the island’s best members are transferred into another island (migration) and replaced by the members of low fitnesses. In the migration phase, the target island is chosen randomly. Examples of large design spaces are utilized to investigate the efficiency of the proposed method. For this purpose, steel are optimized utilizing the proposed method. The results indicate improvements in the available responses

Diseño y explicación del Modelo de Productividad Cuántica en el Ministerio de Deportes y Juventud de la República Islámica de Irán

This study aimed to design a quantum productivity model in the Iranian Ministry of Sport and Youth. The sample was composed of mana-gers and staff of the Ministry of Sport and Youth of the Islamic Republic of Iran. In the first part of the study, 14 people were interviewed through thematic analysis method. In the second part, 188 people completed the questionnaire. The research instruments were an interview and a question-naire of quantum productivity elaborated ad hoc. Data analysis was perfor-med with WarpPLS software. After analyzing the content network, coding and screening, 8 main components of quantum productivity were finally extracted. The prediction power of the designed model was analyzed using the coefficient of determination (R2) for the dependent variables, and it was concluded that the structural model of the present study has sufficient predictive power. Considering these results, it is recommended the use of this quantum productivity model in sports organizations.Resumen: Este estudio tuvo como objetivo diseñar un modelo de productividad cuántica en el Ministerio de Deporte y Juventud de Irán. La muestra estuvo compuesta por gerentes y personal del Ministerio de Deportes y Juventud de la República Islámica de Irán. En la primera parte del estudio, 14 personas fueron entrevistadas a través del método de análisis temático. En la segunda parte, 188 personas completaron el cuestionario. Los instrumentos de investigación fueron una entrevista y un cuestionario de productividad cuántica elaborado ad hoc. El análisis de los datos fue realizado con el software WarpPLS. Después de analizar la red de contenido, la codificación y screening, finalmente se extrajeron 8 componentes principales de productividad cuántica. El poder de predicción del modelo diseñado se analizó utilizando el coeficiente de determinación (R2) para las variables dependientes, y se concluyó que el modelo estructural del presente estudio tiene suficiente poder predictivo. Teniendo en cuenta estos resultados, se recomienda el uso de este modelo de productividad cuántica en organizaciones deportivas