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

Abstract

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).