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Smart Structures and Systems Volume 20, Number 1, July 2017 , pages 99-114 DOI: https://doi.org/10.12989/sss.2017.20.1.099 |
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A new meta-heuristic optimization algorithm using star graph |
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Saeed Asil Gharebaghi, Ali Kaveh and Mohammad Ardalan Asl
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| Abstract | ||
| In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method. | ||
| Key Words | ||
| meta-heuristic algorithm; global optimization; graph theory; optimal design; truss structures; frame structures | ||
| Address | ||
| Saeed Asil Gharebaghi and Mohammad Ardalan Asl: Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran Ali Kaveh: Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran | ||