scholarly journals Pseudodynamic Bearing Capacity Analysis of Shallow Strip Footing Using the Advanced Optimization Technique “Hybrid Symbiosis Organisms Search Algorithm” with Numerical Validation

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Arijit Saha ◽  
Apu Kumar Saha ◽  
Sima Ghosh
Keyword(s):  
Bearing Capacity ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Seismic Loading ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Strip Footing ◽  
Composite Failure ◽  
Engineering Problems ◽  
Dynamic Modules

The analysis of shallow foundations subjected to seismic loading has been an important area of research for civil engineers. This paper presents an upper-bound solution for bearing capacity of shallow strip footing considering composite failure mechanisms by the pseudodynamic approach. A recently developed hybrid symbiosis organisms search (HSOS) algorithm has been used to solve this problem. In the HSOS method, the exploration capability of SQI and the exploitation potential of SOS have been combined to increase the robustness of the algorithm. This combination can improve the searching capability of the algorithm for attaining the global optimum. Numerical analysis is also done using dynamic modules of PLAXIS-8.6v for the validation of this analytical solution. The results obtained from the present analysis using HSOS are thoroughly compared with the existing available literature and also with the other optimization techniques. The significance of the present methodology to analyze the bearing capacity is discussed, and the acceptability of HSOS technique is justified to solve such type of engineering problems.

ROUTE PLANNING AND OPTIMIZATION OF ROUTE USING SIMULATED ANT AGENT SYSTEM

2011 ◽  
Vol 20 (03) ◽  
pp. 457-478 ◽  
Author(s):  
KASHIF ZAFAR ◽  
RAUF BAIG ◽  
NABEEL BUKHARI ◽  
ZAHID HALIM
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Route Planning ◽  
Solution Space ◽  
Optimization Techniques ◽  
Route Optimization ◽  
Complex Environments ◽  
Ant Colony Optimization Algorithm ◽  
Agent System ◽  
Evolutionary Technique

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

scholarly journals An Optimized Protection Coordination Scheme for the Optimal Coordination of Overcurrent Relays Using a Nature-Inspired Root Tree Algorithm

2018 ◽  
Vol 8 (9) ◽  
pp. 1664 ◽  
Author(s):  
Abdul Wadood ◽  
Saeid Gholami Farkoush ◽  
Tahir Khurshaid ◽  
Chang-Hwan Kim ◽  
Jiangtao Yu ◽  
...  
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Operating Time ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Tree Algorithm ◽  
Engineering Problems ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Root Tree

In electrical engineering problems, bio- and nature-inspired optimization techniques are valuable ways to minimize or maximize an objective function. We use the root tree algorithm (RTO), inspired by the random movement of roots, to search for the global optimum, in order to best solve the problem of overcurrent relays (OCRs). It is a complex and highly linear constrained optimization problem. In this problem, we have one type of design variable, time multiplier settings (TMSs), for each relay in the circuit. The objective function is to minimize the total operating time of all the primary relays to avoid excessive interruptions. In this paper, three case studies have been considered. From the simulation results, it has been observed that the RTO with certain parameter settings operates better compared to the other up-to-date algorithms.

Minimization of losses and FACTS installation cost using proposed differential gravitational search algorithm optimization technique

2016 ◽  
Vol 23 (2) ◽  
pp. 235-251
Author(s):  
SN Deepa ◽  
J Rizwana
Keyword(s):  
Power System ◽  
Power Flow ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Optimal Location ◽  
Optimization Techniques ◽  
System Stability ◽  
Gravitational Search ◽  
Installation Cost

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of Unified Power Flow controller for improving voltage profile by reducing losses along with the installation cost thereby enhancing the power system stability. A differential operator is incorporated into the gravitational search algorithm for effective search of the better solution. Due to this, the convergence and accuracy will be faster. The IEEE-6 bus, IEEE-14 bus and IEEE-30 bus systems are tested along with three other optimization techniques to validate the effectiveness of this proposed method. This proposed algorithm presents an optimal location of FACTS devices in transmission lines.

Pseudo-Dynamic Bearing Capacity of Shallow Strip Footing Resting on c-Φ Soil Considering Composite Failure Surface

2015 ◽  
Vol 6 (2) ◽  
pp. 12-34 ◽  
Author(s):  
Arijit Saha ◽  
Sima Ghosh
Keyword(s):  
Bearing Capacity ◽  
Failure Mechanism ◽  
Limit Equilibrium ◽  
Failure Surface ◽  
Friction Angle ◽  
Strip Footing ◽  
Unit Weight ◽  
Composite Failure ◽  
Seismic Accelerations ◽  
Seismic Bearing Capacity

The evaluation of bearing capacity of shallow strip footing under seismic loading condition is an important phenomenon. This paper presents a pseudo-dynamic approach to evaluate the seismic bearing capacity of shallow strip footing resting on c-F soil using limit equilibrium method considering the composite failure mechanism. A single seismic bearing capacity coefficient (N?e) presents here for the simultaneous resistance of unit weight, surcharge and cohesion, which is more practical to simulate the failure mechanism. The effect of soil friction angle(F), soil cohesion(c), shear wave and primary wave velocity(Vs, Vp) and horizontal and vertical seismic accelerations(kh, kv) are taken into account to evaluate the seismic bearing capacity of foundation. The results obtained from the present analysis are presented in both tabular and graphical non-dimensional form. Results are thoroughly compared with the existing values in the literature and the significance of the present methodology for designing the shallow strip footing is discussed.

scholarly journals Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

scholarly journals A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal
Keyword(s):  
Machine Learning ◽  
Design Space Exploration ◽  
Design Space ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Optimization Approach ◽  
Ic Engine ◽  
Weak Learner

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

Layer Separation for Optimization of Composite Laminates

2008 ◽  
Author(s):  
Hossein Ghiasi ◽  
Damiano Pasini ◽  
Larry Lessard
Keyword(s):  
Composite Laminates ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Global Optimum ◽  
Important Variable ◽  
Optimization Techniques ◽  
Thin Layers ◽  
Stacking Sequence ◽  
Layer Separation ◽  
Number Of Layers

The excellent mechanical properties of laminated composites cannot be exploited without a careful design of stacking sequence of the layers. An important variable in the search of the optimum stacking sequence is the number of layers. The larger is this number, the harder as well as longer is the search for an optimal solution. To tackle efficiently such a variable-dimensional problem, we introduce here a multi-level optimization technique. The proposed method, called Layer Separation (LS), increases or decreases the number of layers by gradually separating a layer into two, or by merging two layers into one. LS uses different levels of laminate representation ranging from a coarse level parameterization, which corresponds to a small number of thick layers, to a fine level parameterization, which corresponds to a large number of thin layers. A benefit of such differentiation is an increase of the probability of finding the global optimum. In this paper, LS is applied to the design of composite laminates under single and multiple loadings. The results show that LS convergence rate is not inferior to that of other optimization techniques available in the literature. It is faster than an evolutionary algorithm, more efficient than a layerwise method, simple to perform, and it has the advantage of possibility of termination at any point during the optimization process.

scholarly journals Projectile-target search algorithm: a stochastic metaheuristic optimization technique

2019 ◽  
Vol 9 (5) ◽  
pp. 3772
Author(s):  
Ayong Hiendro
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Search Space ◽  
Global Optimum ◽  
Control Parameters ◽  
Target Search ◽  
Projectile Motion ◽  
Metaheuristic Optimization ◽  
Convergence Process ◽  
Accelerate Convergence

This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithms

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