cuttlefish algorithm

A suitable design is one design can achieve to its aims with minimum cost and needing to less computing time. In civil engineering due to survey of large scale structures and large number of design variables, it is so hard achieving to such design only based on experience and therefore optimization methods came to help designer as useful tools in order to find an economic and efficient design. Structural optimization can be defined as a process of dealing with the optimal design of various structures. Ausual objective function is the weight of the structure. In general, there are three main categories in structural optimization applications, namely, size, topology and geometry (shape) optimization.Cellular automata (CA) is a computationally efficient and robust tool to simply implement complex computations. As CA is simple to be implemented and can deal with complex problems without extensive mathematical computations, it is widely used in various fields of science and engineering.In recent years, various meta-heuristic inspired optimization methods have been developed.Almost all of metaheuristic algorithms come up with an idea of employing a particular process or event in nature as a source of inspiration for the development of optimization algorithm. The Cuttlefish algorithm is inspired based on the color changing behavior of cuttlefish to find the optimal solution. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors.In this article, cuttlefish algorithm (CFA)combined with cellular automata (CA) and were used for optimization truss structures.First, cellular automata and the Moor neighboring cells are defined and to the number ofsquares of the cell number ofcellular automata lattice( )is selected from the  best population. Then, the variables vector and their objective function of selected population are placed in each cell of the cellular automata.In a Moor neighboring, nine cells are compared to each other and the best answer ( )is selected and that is used to create new population.Finally, the best person in thenew population will be selected and itreplacedwith the worst person in the cellular automata, and thus the cellular automatais updated. Some benchmark numerical examples were solved using the CFA and CA-CFA algorithms, and the results of the numerical examples showed that the enhanced algorithm performancesbetter in size and topology optimization of truss structures than cuttlefish algorithm and other methods introduced in the literature. Finally, it can be concluded that the convergence speed of the improved algorithm compared with previous approaches is higher and its ability to achieve the desired values is better too.