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Abstract In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search, are adopted for increasing accuracy and expanding searching space simultaneously. A Gauss distribution function is employed as a technique to enhance the accuracy of this algorithm. POA is evaluated using 23 well-known test functions, 38 IEEE CEC benchmark test functions (CEC 2017, CEC 2019) and three real engineering problems. The statistical results of the benchmark functions show that POA can provide very competitive and promising results. Not only does POA require a relatively short computational time for solving problems, but also it shows superior accuracy in terms of exploiting the optimum.

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads the promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously, whereas Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search local. ES can be easy to deal with its problem via drastic support of Levy flight. As know that the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two trouble (1. Avoiding local optimization, 2. Receiving the result more accurate). The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES in some problems, as well as between experiment and FEM to demonstrate the effectiveness as mentioned. In which natural frequencies is used as the objective function to solve this optimization problem. The results present that the combination of IPSO and ES is quite effective.

The reinforced concrete structure is typical and widely used in many fields. The behavior of concrete is nonlinear and complex. Especially, when cracks/crushings occurred in softening phase. Thus, It is important to find a damaged model of concrete with high reliability in the numerical simulation. The nonlinear behavior of concrete is the most feature used in the simulation. This characteristic is expressed through the parameters defining the yield surface, the flow potential, and the nonlinear relationship of stress-strain in the cases of tension and compression. This paper introduces a damaged concrete model that applies to the simulation of reinforced concrete structures. The reinforced concrete beam and flat slab are selected as examples to evaluate the reliability of the model presented. Through the results achieved, the model used in this paper shows high reliability and can be used to simulate more complex reinforced concrete structures.

Abstract In this paper, a new method in forecasting the horizontal displacement of diaphragm wall (D.W.) for high-rise buildings is introduced. A new stochastic optimizer, called Planet Optimization Algorithm (P.O.A.), is employed to assess how proper finite element (F.E.) simulation is against field data. The process is adopted for a real phased excavation measured at the field. To automatically run the iterative optimization tasks, a source code is constructed directly in the Geotechnical Engineering Software (PLAXIS) by using Python to ensure that the operation between optimization algorithm and F.E. simulations are smooth to guarantee the accuracy of the complex calculation for the soil problem. The proposed process consists of two steps. (1) The parameters will be optimized at the early phases of the excavation. (2) The responses of D.W. displacements are forecasted at the subsequent phases. The aim of the process is to predict the displacements of D.W. of the building from the result of the nearby excavation or to provide early warning about the risks of excavation that may happen under vital phases. The proposed procedure also provides an effective method for optimization-based soil parameters updating in real engineering practice.