An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses

Autor: Eysa Salajegheh, Malihe Jafari, J. Salajegheh
Rok vydání: 2018
Předmět:
Zdroj: Engineering with Computers. 35:781-801
ISSN: 1435-5663
0177-0667
DOI: 10.1007/s00366-018-0631-5
Popis: In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration–exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature.
Databáze: OpenAIRE
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