Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

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

One of the most important topics in geotechnical engineering is seismic analysis of the earth slope. In this study, a pseudo-static limit equilibrium approach is applied for the slope stability evaluation under earthquake loading based on the Morgenstern–Price method for the general shape of the slip surface. In this approach, the minimum factor of safety corresponding to the critical failure surface should be investigated and it is a complex optimization problem. This paper proposed an effective sequential hybrid optimization algorithm based on the tunicate swarm algorithm (TSA) and pattern search (PS) for seismic slope stability analysis. The proposed method employs the global search ability of TSA and the local search ability of PS. The performance of the new CTSA-PS algorithm is investigated using a set of benchmark test functions and the results are compared with the standard TSA and some other methods from the literature. In addition, two case studies from the literature are considered to evaluate the efficiency of the proposed CTSA-PS for seismic slope stability analysis. The numerical investigations show that the new approach may provide better optimal solutions and outperform previous methods

Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

[EN] Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. 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A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Computer Aided Slope Stability Analysis Using Optimization And Parallel Computing Techniques

Slope stability analysis is commonly performed using limit equilibrium methods (LEM). In LEM, factor of safety (FS) is calculated for different trial slip surfaces and the one with the minimum FS is reported as the critical slip surface. Since locating the critical slip surface is believed to be an NP-hard (non-deterministic polynomialtime) problem, heuristic global optimization techniques are employed. Although these techniques have usually produced good results, “No Free Lunch” (NFL) theorems seem to have made the problem of locating the critical slip surface an endless research. According to the NFL theorems, no heuristic optimization technique can perform well for all problems. On the other hand, there may exist other slip surfaces that are as important as the critical slip surface in practical analyses. A slip surface is important, if it is located far away from the critical slip surface, but gives FS close to the minimum FS or the consequences of failure along the slip surface is serious. Therefore, there is a need to constantly implement and test different optimization techniques. However, implementation of optimization techniques in slope stability analysis is often not straightforward because it requires internal links to LEM. Firstly, the present study resolves this issue by developing a decoupled algorithm that allows for easy implementation of optimization techniques. Then, to demonstrate the simplicity of this algorithm and to promote the latest research on slope stability, three state-of-the-art optimization techniques are implemented, and their effectiveness and efficiency in detecting single/multiple global and local minima is investigated on a series of test problems

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved

Optimum design of reinforced concrete cantilever retaining walls according Eurocode 2 (EC2)

This study investigates optimum design in terms of minimum cost of reinforced concrete cantilever retaining walls. For the optimization process, the evolutionary method which is a combination of genetic algorithm and local search techniques was implemented. Evolutionary method was adopted in this study because it can effectively solve highly nonlinear problems and problems that feature discontinuous functions as demonstrated by several works available in the literature. The popularity of the evolutionary method may also be attributed to its availability as one of the solving methods in Solver add-in tool of Microsoft Excel. This implies that it is freely available and no need to pay for extra license to run any optimization problem. The design variables of the problem are thickness of stem wall, thickness of base slab, width of the heel, width of the toe, area of steel reinforcement for the stem wall and base slab. The objective function was to minimise the total cost of the wall, which includes costs of concrete, steel, forming, and excavation. The constrained functions were set to satisfy provisions and requirements of Eurocode 2 (EC2). Material strength and soil characteristics are treated as design parameters where they are kept constants during solution of the problem. Various material cost ratios were considered. Consequently, optimum design charts were developed for a wide range of wall height, coefficient of friction and surcharge load. Following a comprehensive investigation of the minimum cost problems carried out for different cases, one can conclude that the total cost of the retaining wall is directly proportional to the wall height and surcharge load values, whereas, the cost is almost independent of coefficient of friction