Solving Structural Engineering Optimization Problems with an Amalgam Social Cultural Algorithms

This paper presents a new method to solve complex engineering optimization problems. This method is based on intermingling Cultural Algorithms with Tabu search meta-heuristic. The proposed approach uses a specified set of knowledge sources to update agent’s information, as well as employs the diffusion factor to propagate the best solutions in the social network. This approach avoids premature convergence through seeking the best neighboring solutions using Tabu search in case of stagnation, which uses the best solution found so far using social Cultural Algorithms. The Algorithm is validated on a set of structural engineering benchmarks from literature. The obtained results are compared with those generated from other state-of-the-art algorithms in the optimization field. These outcomes are discussed to demonstrate that the technique improves performance in terms of the found optima and convergence speed at less function evaluations. Key-Words: Constrained optimization, Cultural Algorithms, Tabu search, Knowledge swarming, Social integration, Knowledge integration