Modified electromagnetism-like algorithm and its application to slope stability analysis

Abstract

In the view of the disadvantages of complex method (CM) and electromagnetism-like algorithm (EM), complex electromagnetism-like hybrid algorithm (CEM) was proposed by embedding complex method into electromagnetism-like algorithm as local optimization algorithm. CEM was adopted to search the minimum safety factor in slope stability analysis and the results show that CEM holds advantages over EM and CM. It combines the merits of two and is more stable and efficient. For further improvement, two CEM hybrid algorithms based on predatory search (PS) strategies were proposed, both of which consist of modified algorithms and the search area of which is dynamically adjusted by changing restriction. The CEM-PS1 adopts theoretical framework of original predatory search strategy. The CEM-PS2 employs the idea of area-restricted search learned from predatory search strategy, but the algorithm structure is simpler. Both the CEM-PS1 and CEM-PS2 have been demonstrated more effective and efficient than the others. As for complex method which locates in hybrid algorithm, the optimization can be achieved at a convergence precision of 1×10 −3 , which is recommended to use.

Seismic Analysis of Earth Slope Using a Novel Sequential Hybrid Optimization Algorithm

“…As a result, a number of research have been conducted in order to improve the performance and efficiency of the original algorithms in some aspects and to apply them to a specific application. [9][10][11][12][13][14] Tunicate Swarm Algorithm (TSA) is a recently developed bioinspired meta-heuristic optimization technique that is firstly proposed by Kaur et al [15] in 2020. Tunicates employ swarm intelligence and jet propulsion at sea to find the best state in their environment for finding food.…”

The Research of Oil & Gas Energy Saving Index Prediction Based on Neural Network and Quantum-Behaved Particle Swarm Optimization

“…It [2] proposed prediction method that based on support vector machine and chaos particle swarm optimization process index, the method of the model precision meet the specific site process standards. It [3] proposed modeling methods that based on the neural network and multivariate statistical analysis of the dynamic prediction, It is improved to a single sequence network prediction, It improves the performance of the network [4][5][6] .Because of the above methods or depend on professional knowledge, or only applies to certain special circumstances.. This paper presents prediction method that based on BP neural network and QPSO, It effective use of the BP neural network mobility advantage, And the method to solve the BP neural network itself are easy to fall into the local minimum problem and the problem of slow convergence speed, It realize both advantages coexist, It reached the ideal test results, This method through a production water injection pump unit consumption indicators for training data, It proved the feasibility of this method.…”