scholarly journals The effect of maximum iteration using 3 dimensional limit equilibrium method in open pit mine

2021 ◽  
Vol 894 (1) ◽  
pp. 012033
Author(s):  
F M Syahputra ◽  
M A Azizi ◽  
I Marwanza
Keyword(s):  
Particle Swarm Optimization ◽  
Limit Equilibrium ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Limit Equilibrium Method ◽  
Search Method ◽  
Swarm Optimization ◽  
3 Dimensional ◽  
Iteration Number ◽  
Compute Time

Abstract Nickel ore mines have a high potential of landslides due to their weak material base, which consists of soil. It is caused by the increase of soil density in rain conditions, leading to decreased soil shear strength (c) and internal friction angle (ϕ). This research aims to determine the optimum value of the maximum iteration number based on the Cuckoo Search and Particle Swarm Optimization search method. In this research, the slope is analyzed using the 3 Dimensional limit equilibrium method “Simplified Bishop,” a slope stability analysis method that uses the principle of static equilibrium. Alongside this method, the Cuckoo Search and Particle Swarm Optimization is adopted. The Cuckoo Search and Particle Swarm Optimization are metaheuristic optimization techniques used as the slipped surface search method. Series of 3-dimensional limit equilibrium computation is performed using different amounts of nests in the cuckoo search method and different particle values and maximum iteration number. Cuckoo Search method to achieve optimal nest 100 and iteration of 80 with the fastest compute time of 3 minutes 49 seconds. While the Particle Swarm Optimization to achieve optimal on particles 60, iteration as much as 480 with a compute time of 6 minutes 46 second, with a factor of safety value of 1,12.

scholarly journals Particle Swarm Optimization Algorithm Coupled with Finite Element Limit Equilibrium Method for Geotechnical Practices

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hongjun Li ◽  
Hong Zhong ◽  
Zuwen Yan ◽  
Xuedong Zhang
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Factor Of Safety ◽  
Particle Swarm Optimization Algorithm ◽  
Limit Equilibrium ◽  
Particle Swarm ◽  
Limit Equilibrium Method ◽  
Swarm Optimization ◽  
Equilibrium Method

This paper proposes a modified particle swarm optimization algorithm coupled with the finite element limit equilibrium method (FELEM) for the minimum factor of safety and the location of associated noncircular critical failure surfaces for various geotechnical practices. During the search process, the stress compatibility constraints coupled with the geometrical and kinematical compatibility constraints are firstly established based on the features of slope geometry and stress distribution to guarantee realistic slip surfaces from being unreasonable. Furthermore, in the FELEM, based on rigorous theoretical analyses and derivation, it is noted that the physical meaning of the factor of safety can be formulated on the basis of strength reserving theory rather than the overloading theory. Consequently, compared with the limit equilibrium method (LEM) and the shear strength reduction method (SSRM) through several numerical examples, the FELEM in conjunction with the improved search strategy is proved to be an effective and efficient approach to routine analysis and design in geotechnical practices with a high level of confidence.

Performance comparison of particle swarm optimization and Cuckoo search for online route planning

2018 ◽  
Vol 33 (10) ◽  
pp. 40-50 ◽  
Author(s):  
German David Goez-Sanchez ◽  
Jorge Alberto Jaramillo-Garzon ◽  
Ricardo Andres Velasquez
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Route Planning ◽  
Cuckoo Search ◽  
Performance Comparison ◽  
Swarm Optimization

A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

2011 ◽  
Vol 15 (4) ◽  
pp. 473-478
Author(s):  
Shamshul Bahar Yaakob ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Method ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Selection Problems ◽  
Hybrid Particle Swarm Optimization ◽  
Trade Offs

In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposed method is a viable approach to portfolio selection.

scholarly journals Research on sensory comfort of tight-fitting sportswear based on intelligent models

2021 ◽  
Vol 16 ◽  
pp. 155892502110680
Author(s):  
Pengpeng Cheng ◽  
Jianping Wang ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Xuyuan Tao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Analytic Hierarchy Process ◽  
Rough Set Theory ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Entropy Weight ◽  
Fuzzy Rough Set ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Hierarchy Process

In order to study the influence of human body parts on the overall comfort under different sports conditions, this paper designed a series of actions such as standing, squatting, running, walking, and so on, and obtained the key parts that affected the overall comfort at every experimental stage (i.e. every motion state) through subjective evaluation. That is, to study and analyze the comfort evaluation of every part and the whole body under different motions conditions, as well as the main parts that affect the overall comfort. In this paper, Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model, and Particle Swarm Optimization-Cuckoo Search were used to optimize the position index. At last, the prediction model of overall comfort was established by Adaptive Network-based Fuzzy Influence System. The input parameters are body part indexes screened by Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model and Particle Swarm Optimization-Cuckoo Search, respectively. And the output is the overall comfort evaluation value. Compared with the real value of overall comfort in every experimental stage, the effectiveness of Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model, and Particle Swarm Optimization-Cuckoo Search optimizing indexes is verified. The results show that: (1) About index optimization models, Particle Swarm Optimization-Cuckoo Search and Analytic Hierarchy Process-Entropy weight are better than Fuzzy-Rough Set Theory, so both Particle Swarm Optimization-Cuckoo Search and Analytic Hierarchy Process-Entropy weight could optimize index predicting overall comfort. (2) Different movements have great differences in the parts that affect the overall comfort.

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