scholarly journals Control of diversity in genetic algorithms using multimodal strategies

2019 ◽  
Vol 13 (1) ◽  
pp. 144-151
Author(s):  
Henry Alberto Hernández Martínez ◽  
Lely Adriana Luengas Contreras
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Heuristic Search ◽  
Population Diversity ◽  
Optimization Techniques ◽  
Mathematical Functions ◽  
Previous Solution ◽  
Design Tradeoffs ◽  
Conventional Genetic Algorithm ◽  
Better Than

An optimization process is a kind of process that systematically comes up with solutions that are better than a previous solution used before. Optimization algorithms are used to find solutions which are optimal or near-optimal with respect to some goals, to evaluate design tradeoffs, to assess control systems, to find patterns in data, and to find the optimum values (local or global) of mathematical functions. A genetic algorithm is one of the optimization techniques. In this way, a heuristic search that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation which are population algorithms that emulate behavior similar to Darwinian natural selection. Taking into account these issues, this article shows the performance of a genetic algorithm designed, which allows to find several minimums within a function from the control of population diversity. To perform the tests, the algorithm with four different functions was used, with the particularity of having several minima with the same value. Proposed strategy was compared with a conventional genetic algorithm, the result was the conventional one can only find some of the minimums of the function and sometimes only one, while the proposal finds most of the minimums

Parallel Dynamic Optimization of Steel Risers

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Rafael L. Tanaka ◽  
Clóvis de A. Martins
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Natural Selection ◽  
Dynamic Optimization ◽  
Solution Space ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Definition Of

This paper addresses the use of optimization techniques in the design of a steel riser. Two methods are used: the genetic algorithm, which imitates the process of natural selection, and the simulated annealing, which is based on the process of annealing of a metal. Both of them are capable of searching a given solution space for the best feasible riser configuration according to predefined criteria. Optimization issues are discussed, such as problem codification, parameter selection, definition of objective function, and restrictions. A comparison between the results obtained for economic and structural objective functions is made for a case study. Optimization method parallelization is also addressed.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

FURTHER STUDIES ON NATURAL SELECTION AMONG HYBRIDS OF CARDUUS ACANTHOIDES AND CARDUUS NUTANS

1964 ◽  
Vol 42 (12) ◽  
pp. 1605-1613 ◽  
Author(s):  
R. J. Moore ◽  
G. A. Mulligan
Keyword(s):  
Natural Selection ◽  
Chromosome Number ◽  
Parental Species ◽  
Experimental Studies ◽  
Great Decrease ◽  
Carduus Acanthoides ◽  
Carduus Nutans ◽  
Experimental Plots ◽  
Better Than ◽  
Made In

A third 5-year survey made in 1962 of Carduus acanthoides, C. nutans, and their hybrids in Grey Co., Ontario, revealed that a great decrease in these populations had occurred. C. acanthoides and hybrids similar to this species had survived better than C. nutans but very little spread of either species seemed to have occurred in 1957–1962. In experimental plots the hybrid has been made and backcrossed to the parental species. The species differ in chromosome number (C. acanthoides, 2n = 22; C. nutans, 2n = 16) and hybrids have intermediate numbers. Evidence was found from field and experimental studies that the progeny of the F1 hybrid included a greater proportion of seedlings with the higher chromosome numbers than with the lower and intermediate numbers. It is suggested that this selection may operate through the rejection of the longer chromosomes received from C. nutans, which, in certain zygotic combinations may constitute an excess of chromatin lethal to the zygote.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

scholarly journals Optimization of spectrum sensing in cognitive radio using genetic algorithm

2012 ◽  
Vol 25 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Rashmi Deka ◽  
Soma Chakraborty ◽  
Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Rapid Development ◽  
Probability Of Detection ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Space Allocation ◽  
Wireless Environment ◽  
Better Than

Spectrum availability is becoming scarce due to the rise of number of users and rapid development in wireless environment. Cognitive radio (CR) is an intelligent radio system which uses its in-built technology to use the vacant spectrum holes for the use of another service provider. In this paper, genetic algorithm (GA) is used for the best possible space allocation to cognitive radio in the spectrum available. For spectrum reuse, two criteria have to be fulfilled - 1) probability of detection has to be maximized, and 2) probability of false alarm should be minimized. It is found that with the help of genetic algorithm the optimized result is better than without using genetic algorithm. It is necessary that the secondary user should vacate the spectrum in use when licensed users are demanding and detecting the primary users accurately by the cognitive radio. Here, bit error rate (BER) is minimized for better spectrum sensing purpose using GA.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Sign in / Sign up

Export Citation Format

Share Document