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In this research paper, a new surrogate-assisted metaheuristic for shape optimization is proposed. A seagull optimization algorithm (SOA) is used to solve the shape optimization of a vehicle bracket. The design problem is to find structural shape while minimizing structural mass and meeting a stress constraint. Function evaluations are carried out using finite element analysis and estimated by using a Kriging model. The results show that SOA has outstanding features just as the whale optimization algorithm and salp swarm optimization algorithm for designing optimal components in the industry.

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This paper proposes an alternative meshless approach to solve partial differential equations (PDEs). With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA) is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE) is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced.