Software for Optimum Gear Design (DISENG)

1992 ◽  
Author(s):  
Juan Carlos Jáuregui ◽  
Rodrigo López Sansalvador ◽  
Alfredo Ramos Aparicio ◽  
Isaías Regalado Contreras
Keyword(s):  
Input Data ◽  
Search Algorithm ◽  
Solution Space ◽  
Optimization Techniques ◽  
Gear Design ◽  
Specialized Software ◽  
Intermediate Results

Abstract In this paper, a specialized software for optimum gear design is presented. This optimum is found through a logical solution search algorithm, rather than general optimization techniques. The logical search is based upon the constraints and needs set by the user, who defines the solution space and the search trajectory. For each problem, the solution will be the less expensive according to specific constraints. Calculation routines were based on AGMA standards, and input data routines were conceived to be as helpful as possible. A complete help routine is included on each part of the software and every input data is foolproof. It also includes a routine for intermediate results analysis that allows the user to verify every iteration.

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 Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

scholarly journals Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

2017 ◽  
Vol 7 (5) ◽  
pp. 2349 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

scholarly journals Parametric estimation in photovoltaic modules using the crow search algorithm

2022 ◽  
Vol 12 (1) ◽  
pp. 82
Author(s):  
Oscar Danilo Montoya ◽  
Carlos Alberto Ramírez-Vanegas ◽  
Luis Fernando Grisales-Noreña
Keyword(s):  
Numerical Simulations ◽  
Search Algorithm ◽  
Short Circuit ◽  
Optimization Technique ◽  
Solution Space ◽  
Open Circuit ◽  
Parametric Estimation ◽  
Convex Optimization Problem ◽  
Processing Times ◽  
Objective Function Values

<p>The problem of parametric estimation in photovoltaic (PV) modules considering manufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R<sub>p</sub> and R<sub>s</sub>)), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 × 10<sup>−28</sup> and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.</p>

scholarly journals An Effective System of Intrusion Detection on Deep Neural Network by Hybrid Optimization in Cyber Security

2019 ◽  
Vol 9 (1) ◽  
pp. 1320-1327
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Search Algorithm ◽  
Detection System ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
Experimental Result ◽  
Gray Wolf ◽  
High Detection

In present trends organizations are very much interested to protect data and prevent malware attack by using well flourished and excellent tools. Many algorithms are used for the intrusion detection system (IDS) and it has pros and cons. Here we proposed a novel method of intrusion detection using hybrid optimization techniques such as Gravity search algorithm with gray wolf optimization (GSGW). In this method the gray wolf technique has a leader for the continuous monitoring of the attacker and has a low false alarm rate and a high detection rate. The performance evaluation is done by the feature selection in NSL-KDD dataset. In the proposed method the experimental result reveals less false alarm rate, better accuracy and high Detection when compared to previous analysis.

Design and Implementation of Tuning PID Controller using Modern Optimization Techniques for Micro-Robotics System

2021 ◽  
Vol 10 (11) ◽  
pp. 51-68
Author(s):  
Ehab S. Ghith ◽  
◽  
Mohamed Sallam ◽  
Islam S. M. Khalil ◽  
Mohamed Youssef Serry ◽  
...  
Keyword(s):  
Steady State ◽  
Optimization Problems ◽  
Model Simulation ◽  
Search Algorithm ◽  
Optimal Parameter ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Control Technique ◽  
Pid Controllers ◽  
Flower Pollination Algorithm

One of the main difficult tasks in the field of micro-robotics is the process of the selection of the optimal parameters for the PID controllers. Some methods existed to solve this task and the common method used was the Ziegler and Nichols. The former method require an accurate mathematical model. This method is beneficial in linear systems, however, if the system becomes more complex or non-linear the method cannot produce accurate values to the parameters of the system. A solution proposed for this problem recently is the application of optimization techniques. There are various optimization techniques can be used to solve various optimization problems. In this paper, several optimization methods are applied to compute the optimal parameter of PID controllers. These methods are flower pollination algorithm (FPA), grey wolf optimization (GWO), sin cosine algorithm (SCA), slime mould algorithm (SMA), and sparrow search algorithm (SSA). The fitness function applied in the former optimization techniques is the integral square Time multiplied square Error (ISTES) as the performance index measure. The fitness function provides minimal rise time, minimal settling time, fast response, and no overshoot, Steady state error equal to zero, a very low transient response and a non-oscillating steady state response with excellent stabilization. The effectiveness of the proposed SSA-based controller was verified by comparisons made with FPA, GWO, SCA, SMA controllers in terms of time and frequency response. Each control technique will be applied to the identified model (simulation results) using MATLAB Simulink and the laboratory setup (experimental results) using LABVIEW software. Finally, the SSA showed the highest performance in time and frequency responses.

Real-Time Implementation for Tuning PID Controller Based on Advanced Optimization Techniques for Micro-Robotics System

2021 ◽  
Vol 10 (6) ◽  
pp. 161-173
Author(s):  
Ehab S. Ghith ◽  
◽  
Mohamed Sallam ◽  
Islam S. M. Khalil ◽  
Mohamed Serry ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Pid Controller ◽  
Model Simulation ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Control Technique ◽  
Flower Pollination Algorithm ◽  
Labview Software ◽  
Laboratory Setup ◽  
Sine Cosine Algorithm

The process of tuning the PID controller’s parameters is considered to be a difficult task. Several approaches were developed in the past known as conventional methods. One of these methods is the Ziegler and Nichols that relies on accurate mathematical model of the linear system, but if the system is complex the former method fails to compute the parameters of PID controller. To overcome this problem, recently there exist several techniques based on artificial intelligence such as optimization techniques. The optimization techniques does not require any mathematical model and they are considered to be easy to implement on any system even if it complex, can reach optimal solutions on the parameters. In this study, a new approach to control the position of the micro-robotics system proportional - integral - derivative (PID) controller is designed and a recently developed algorithm based on optimization is known as the sparrow search algorithm (SSA). By using the sparrow search algorithm (SSA), the optimal PID controller parameters were obtained by minimizing a new objective function, which consists of the integral square Time multiplied square Error (ISTES) performance index. The effectiveness of the proposed SSA-based controller was verified by comparisons made with the Sine Cosine algorithm (SCA), and Flower pollination algorithm (FPA) controllers in terms of time and frequency response. Each control technique will be applied to the identified model (simulation results) using MATLAB Simulink and the laboratory setup (experimental results) using LABVIEW software. Finally, the SSA showed the highest performance in time and frequency responses.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

2012 ◽  
pp. 179-200 ◽  
Author(s):  
Cristiane G. Taroco ◽  
Eduardo G. Carrano ◽  
Oriane M. Neto
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Efficient Solutions ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Robust Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

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