A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

A Novel Cultural Algorithm based on Particle Swarm Optimization and Whale Optimization Algorithm

An ameliorative cultural algorithm (CA) based on particle swarm optimization (PSO) and whale optimization algorithm (WOA) is raised (CA-PSOWOA), so as to conquer the defects of WOA and PSO, such as poor global exploration ability and easy fall into local optimal solution. Firstly, a nonlinear inertia weight strategy is leaded to optimize the PSO and WOA, then CA is introduced to regulate the ability of global exploration and local exploitation of PSO and WOA. By testing on benchmark functions, it is proved that CA-PSOWOA improves the global exploration ability and solution accuracy, and its performance is better than the traditional PSO and WOA, and other algorithms.

Sidelobe level minimization for uniform circular smart antenna array using cultural algorithm

Cultural algorithm (CA) is a new evolutionary program inspired by sociology and archaeology theories that assisting formulating cultural evaluation. Its use to solve optimization problems. This paper analyzed the beamforming of a uniform circular antenna array (UCAA) via using the CA algorithm. The sidelobe level (SLL) is minimized by adjusting the appropriate weight for each element. In addition, the optimal beam pattern is achieved by using CA for UCAA, which means that the main beam is steering to the desired user, while the nulls represent the interference signals. The excitation amplitude is supposed to be constant while the elements are assumed isotropic. The circular array number elements and the interspacing distance between them are setting as optimization parameters. The simulation results show that the CA rationally reacts to the changing environments, and it is valuable for SLL reduction. A −25 dB of relative SLL is achieved under beam scanning (0º) and (15º), respectively.

Performance assessment of meta-heuristics for composite layup optimisation

This paper investigates the performance of several meta-heuristic algorithms, including Particle Swarm Optimisation (PSO), different variants of Differential Evolution (DE), Biogeography-Based Optimisation (BBO), Cultural Algorithm (CA), Optics-Inspired Optimisation (OIO), and League Championship Algorithm (LCA), for optimum layup of laminated composite plates. The study provides detailed Pseudo codes for different algorithms. The buckling capacity maximisation of a 64-layer laminated composite plate under various load scenarios has been considered as the benchmark problem, in which the design variables are the stacking sequences of layers. A Deep Statistical Comparison (DSC) method is employed to rank the performance of different algorithms. The DSC uses a nonparametric two-sample Kolmogorov-Smirnov test to conduct the performance comparisons between the algorithms. The overall performance rankings obtained from the DSC suggest that the LCA, OIO, and PSO algorithms perform remarkably better in comparison to other algorithms. The comparisons provide some interesting conclusions on the performance of different algorithms.

Research on the framework of traditional culture innovation system based on artificial intelligence

In order to improve the effect of traditional cultural innovation, this paper proposes a cultural algorithm with dual knowledge, and improves the effect of the algorithm to obtain a cultural algorithm with dual knowledge. Each individual corresponds to its unique dual knowledge, so that the individual’s evolution can move towards the current optimal solution. This paper constructs a traditional cultural innovation system architecture based on artificial intelligence, analyzes its functional modules, and constructs the system structure from the perspectives of cultural classification and cultural innovation. After constructing the system, this paper designs experiments to verify the system performance. The research results show that the system constructed in this paper performs well in traditional cultural analysis and traditional cultural innovation, and can provide references for related research.