Science topic

# Genetic Algorithm - Science topic

Genetic Algorithm are solving problems in maths by optimization technique using GA

Questions related to Genetic Algorithm

Optimization by genetic algorithm (GAO) in electricity and smart grid?

why we use it ?

what is the process of this algorithms ?

I am using a genetic algorithm to solve a multivariable optimization problem. The difficulty in exploring all the solutions is that the permissible set of each variable of the solution is of the form {0} U [a,b], where 0 < a < b (the magnitudes are around a=4 and b=15). "Solutions" that do not satisfy the constraints get a low fitness. So when the genetic explores the search space, it is difficult that it tries solutions with one variable at 0 (zero). I can try to enlarge the interval around 0 and to modify the fitness of variables close to zero. Does anybody know how to treat this kind of constraints? By the way I am using the DEAP genetic algorithms, more precisely this one: http://deap.gel.ulaval.ca/doc/default/examples/ga_onemax.html.

I constantly use genetic algorithm and neural network , if you know and examine a better method to find when the data is high dimensional .

I have a thesis right now, I don't know if its possible to do, I'm trying to create a website builder, but instead of using Draggable Pre-Templates or Libraries, I would make a UI Component Generator with Different Properties and Designs.

But as I did some research, I realized its going to be messed up upon generation, I wanted it Linear in sequence and not just random Components with Random Designs, I wanted an organized linear pattern of generated UI Components. and I was thinking of using Seeds to find previously generated UI Components and saving it in a History Panel of panel. and being able to search it.

Needs some opinions and ideas because we're blasting our way to graduation..

Thank you! any help is appreciated!

**If ChatGPT is merged into search engines developed by internet technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be involved?**

Leading Internet technology companies that also have and are developing search engines in their range of Internet information services are working on developing technological solutions to implement ChatGPT-type artificial intelligence into these search engines. Currently, there are discussions and considerations about the social and ethical implications of such a potential combination of these technologies and offering this solution in open access on the Internet. The considerations relate to the possible level of risk of manipulation of the information message in the new media, the potential disinformation resulting from a specific algorithm model, the disinformation affecting the overall social consciousness of globalised societies of citizens, the possibility of a planned shaping of public opinion, etc. This raises another issue for consideration concerning the legitimacy of creating a control institution that will carry out ongoing monitoring of the level of objectivity, independence, ethics, etc. of the algorithms used as part of the technological solutions involving the implementation of artificial intelligence of the ChatGPT type in Internet search engines, including those search engines that top the rankings of Internet users' use of online tools that facilitate increasingly precise and efficient searches for specific information on the Internet. Therefore, if, however, such a system of institutional control on the part of the state is not established, if this kind of control system involving companies developing such technological solutions on the Internet does not function effectively and/or does not keep up with the technological progress that is taking place, there may be serious negative consequences in the form of an increase in the scale of disinformation realised in the new Internet media. How important this may be in the future is evident from what is currently happening in terms of the social media portal TikTok. On the one hand, it has been the fastest growing new social medium in recent months, with more than 1 billion users worldwide. On the other hand, an increasing number of countries are imposing restrictions or bans on the use of TikTok on computers, laptops, smartphones etc. used for professional purposes by employees of public institutions and/or commercial entities. It cannot be ruled out that new types of social media will emerge in the future, in which the above-mentioned technological solutions involving the implementation of ChatGPT-type artificial intelligence into online search engines will find application. Search engines that may be designed to be operated by Internet users on the basis of intuitive feedback and correlation on the basis of automated profiling of the search engine to a specific user or on the basis of multi-option, multi-criteria search controlled by the Internet user for specific, precisely searched information and/or data. New opportunities may arise when the artificial intelligence implemented in a search engine is applied to multi-criteria search for specific content, publications, persons, companies, institutions, etc. on social media sites and/or on web-based multi-publication indexing sites, web-based knowledge bases.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

*If ChatGPT is merged into search engines developed by online technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be associated with this?*

What is your opinion on the subject?

What do you think about this topic?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

Hello experts

I am dealing with an optimization problem in which the algorithm will choose the section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).

How can I ask the algorithm to change the cross section if the condition of the period isn't satisfied?

N.B: the period value is retrieved from the sap2000 software using the API, and it has no relation with the design variables to be set as a constriant.

Any one knows how to do it please?

Hi everyone,

I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that are assigned to genes of the chromosomes or all blocks of the ultimate pit?

I don't know any normalization method for obtaining a feasible sequencing plan, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail! because when it is assigned a time period to blocks, there is any rule to regard the condition of 45 degrees on slopes!!!

I want to explain more about the MATLAB code I wrote. Precedence constraints (These constraints ensure that a block can only be extracted during some time period t if all of its predecessor blocks have been mined completely before or during time period t) are not true, I think because when I import the results into Surpac software, the extraction of blocks and sequencing of them seems like strip mining regardless of 45 degrees on slopes and instead of formation of inverse cones! I don't know how can I implement the normalization process you mentioned above?!

I attach an output file of MATLAB so you can see.

**Thank you in advance**

Please I need help me with some open research problems in genetic algorithm relative to computer vision.

Hello,

I am trying to build an (r,Q) inventory control model , to be implemented in a local manufacturing firm. In my model, r is identified by studying the demand trends and fitting normal distributions, while Q is identified by genetic algorithm minimising an inventory cost function.

Firstly the list of parts for re-order are first ideintified, after which a genetic algorithm co-optimises the batch sizes across all parts on re-order, considering factory capacity and demand constraints.

In my simulations, I find that the Q values suggested by my algorithm are realistic and in-line with what is required, however, it turns out that the model identifies all the parts which are less likely to be ordered by the production team.

Would anyone be able to shed any light on how to control this ?

I am studying an article about implementation of genetic algorithm. I have uploaded the parameters for this implementation.

As I know, in roullete selection method, probability of selecting individuals is based on their cost function values. the more optimum individual has the higher probability of being selected. So, what is the meaning of selection probability here?

Here is the link to the article:

Dear All,

Could you please inform regarding software for the metaheuristic algorithms that are compatible with Ms. Excel (add-ins) and free (or not).

a) Genetic algorithm (GA) = ???

b) Particle swarm-optimization (PSO) = ???

c) Bee colony optimization (BCO) = ???

d) etc..

I have been searching for genetic algorithm a lot, But I could not understand necessity of using crossover and mutation simultaneously yet. In an online course, following paragraph was written:

crossover is an operation which drive the population towards a local maximum(or minimum). If we use only crossover, it will yield approximately the same result as hill-climbing algorithm!!!mutation is a so-called divergence operation force one or more individuals of the population to discover other regions of the search space. So, this is essential in order to find the global optimum.

I can not understand it easily, especially because in metaheuristic algorithms, we must cope with somehow statistical-based optimization. Moreover, I had implemented GA in python and still can not realize the performance difference between these two parameters.

Hi everyone,

I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that I assigned to genes of the chromosomes or all blocks of the ultimate pit? I don't know any normalization methods for obtaining a feasible sequencing, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail!

I would appreciate your guidance and help, in advance!

Dear friend:

To reduce feature dimension and improve the model performance in machine learning, I am using the genetic algorithm to chose the best 20 features form 100 feature subset. However,

**some feaures are presented more than one in a chromosome**, what is the best method to deal with this issue?Best wishes!

WangChao

Hello experts

This question is shared with one of my research team.

We are dealing with an optimization problem in which the algorithm will choose the cross-section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).

We know that the obtained optimal values for the section can be for example 330mm.

Recently, we came across this phrase in a paper dealing with the same problem where the author stated that 'The column sections are square with dimensions of 250 mm × 250 mm to 1200 mm × 1200 mm, by a step of 50 mm'

Our concern is how to oblige the algorithm to choose 50mm for the next candidate within the design interval.

Anyone knows how to do it, please?

**How should the learning algorithms contained in ChatGPT technology be improved so that the answers to questions generated by this form of artificial intelligence are free of factual errors and fictitious 'facts'?**

**How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?**

The ChatGPT artificial intelligence technology generates answers to questions based on an outdated set of data and information downloaded from selected websites in 2021. In addition, the learning algorithms contained in ChatGPT technology are not perfect, which means that the answers to questions generated by this form of artificial intelligence may contain factual errors and a kind of fictitious 'facts'. A serious drawback of using this type of tool is that the ChatGPT-generated answers may contain serious factual errors. When people ask about something specific, they may receive an answer that is not factually correct. ChatGPT often answers questions eloquently, but often its answers may not relate to existing facts. ChatGPT can generate a kind of fictitious 'facts', i.e. the generated answers may contain stylistically, phraseologically, etc. correctly formulated sentences containing descriptions and characteristics of certain objects presented as real existing but not actually existing. In the future, a ChatGPT-type system will be refined and improved, which will be self-learning and self-improving in the analysis of large data sets and will take into account newly emerging data and information to generate answers to the questions asked without making numerous mistakes as is currently the case.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

*How should the learning algorithms contained in the ChatGPT technology be improved so that the answers generated by this form of artificial intelligence to the questions asked are free of factual errors and fictitious "facts"?*

*How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?*

What do you think about this subject?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best regards,

Dariusz Prokopowicz

How to use a genetic algorithm for optimizing the Artificial Neural Network model in R.

In genetic algorithms, if two parents are two graphs or two trees, instead of two sequences, then how to design the crossover operator?

How can I calculate

**ANOVA table**for the**quadratic**model by**Python**?I want to calculathe a table like the one I uploaded in the image.

Few researches have stated that a multi population genetic algorithm avoids premature convergence as opposed to the conventional GA. Does multipopulation GA offer any other advantages over the single population GA? Also, is using a large population size for GA better than multipopulations?

I am trying to use the Genetic Algorithm to optimise the strategies for containing the spread of COVID-19. The strategies include (i) travel restrictions, (ii) lock-down, and (iii) wearing of nose masks. The model is formulated as an SEIR compartmentalized model.

I have spent a lot of time trying to learn how to do that using MATLAB but I have not succeeded.

I will be grateful if anyone can share their MATLAB codes with me.

How to use genetic algorithm in predicting some values based on csv file

Over the last few decades, there have been numerous metaheuristic optimization algorithms developed with varying inspiration sources. However, most of these metaheuristics have one or more weaknesses that affect their performances, for example:

- Trapped in a local optimum and are not able to escape.
- No trade-off between the exploration and exploitation potentials
- Poor exploitation.
- Poor exploration.
- Premature convergence.
- Slow convergence rate
- Computationally demanding
- Highly sensitive to the choice of control parameters

Metaheuristics are frequently improved by adding efficient mechanisms aimed at increasing their performance like opposition-based learning, chaotic function, etc. What are the best efficient mechanisms you suggest?

Dear community,

I am planning on conducting a GWAS analysis with two groups of patients differing in binary characteristics. As this cohort naturally is very rare, our sample size is limited to a total of approximately 1500 participants (low number for GWAS). Therefore, we were thinking on studying associations between pre-selected genes that might be phenotypically relevant to our outcome. As there exist no pre-data/arrays that studied similiar outcomes in a different patient cohort, we need to identify regions of interest bioinformatically.

1) Do you know any tools that might help me harvest genetic information for known pathways involved in relevant cell-functions and allow me to downscale my number of SNPs whilst still preserving the exploratory character of the study design? e.g. overall thrombocyte function, endothelial cell function, immune function etc.

2) Alternatively: are there bioinformatic ways (AI etc.) that circumvent the problem of multiple testing in GWAS studies and would allow me to robustly explore my dataset for associations even at lower sample sizes (n < 1500 participants)?

Thank you very much in advance!

Kind regards,

Michael Eigenschink

i am using genetic algorithm. when i am testing its performance, i found my code doesn't work as it should be. when I am changing the weights from 1 to -1 (Maximize to minimize), my code shows the exact same results.

my code is as follows:

def train_evaluate(individual_solution):

x_bits = BitArray(individual_solution[0:4])

y_bits = BitArray(individual_solution[4:])

x = x_bits.uint

y = y_bits.uint

return x+y ,

population_size = 6

num_generations = 10

gene_length = 8

creator.create('FitnessMin', base.Fitness, weights = (-1.0,))

**## I only change this parameter**creator.create('Individual', list , fitness = creator.FitnessMin)

toolbox = base.Toolbox()

toolbox.register('binary', bernoulli.rvs, 0.5)

toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.binary, n = gene_length)

toolbox.register('population', tools.initRepeat, list , toolbox.individual)

toolbox.register('select', tools.selRoulette)

toolbox.register('mate', tools.cxOrdered)

toolbox.register('mutate', tools.mutShuffleIndexes, indpb = 0.2)

toolbox.register('evaluate', train_evaluate)

population = toolbox.population(n = population_size)

r, log = algorithms.eaSimple(population, toolbox, cxpb = 0.4, mutpb = 0.1, ngen = num_generations, verbose = False)

tools.selBest(population,k = 1)

when I test weights = (1, )

the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]

when I test weights = (-1, )

the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]

great thanks if you could kindly help me.

Hello,

I found this approach

**in a paper recently published, in which the author tried to describe a new way to tick a behaviour tree instead of using the conventional approach, in order to evolve a behaviour for an agent (game agent) using genetic programming.***-the per-node approach-*I would like to discuss this approach and how it works deeply.

If anyone could share any idea about this, it will be more productive.

Thank you.

In his name is the judge

Hi

There is a fuzzy logic control system in python. The system contain 2 inouts and 18 outputs.

inference of system is mamdani and shape function used to be guassian.

Then in term of refine performance of the controller I need to optimize specifications belong to shape functions of both input and output. In order to that I need to use multi objective optimization.

We have 2 input and 1 output in case of this problem. I have developed 3 shape functions for each entrance and 3 for output and the shape function is gaussian so we have 18 parameters totally.

I defined my problem as a function in python. But notice this I there is not any clear relationship between input and output of function. It’s just a function which is so complicated with 2 inputs and 18 outputs.

I made my decision to use NSGAII algorithm and I really don't want to change the algorithm.

So I try every way to optimize my function but I didn’t find any success. By searching about python library which can do multiobjective optimization I find Pymoo as the best solution but I really failed to optimize my function which is complicated custom function, with it.

So It’s really my pleasure if you can introduce me a library in python or suggest me a way that I can use Pymoo in order to this aim.

wish you best

Take refuge in the right.

Hello,

I'm working with a large number of L*a*b* colour points from ceramic samples, and I'm interested in reconstructing possible spectrums that could have made those colours. There's more than one possibility, of course, but my plan was to use a genetic algorithm approach to find several answers at a distance from each other to set out the solution space. The better candidates are all positive, and reasonably smooth as I'm dealing with natural clays, not sharply pigments goods.

The problem is that I'm currently unable to get closer than about 3units to the target colour, and often no closer than 10-15 units. This could be for the following reasons, is there anything else I've missed?

1) Solution space pixelation - I'm working with 5nm wide bands, so there are only 81 bands in the spectrum, only a fraction of which matter for the specific colour, and it's possible that there's no close solution at this resolution

2) many local maximas - it's possible the solution space is very unsmooth, and the algorithm is latching onto the 'wrong' one too early. The evidence for this is that runs frequently 'lock' quite early on, but repeated runs give different results.

3) something else...

Hi,

What is the different between 'fitness function score' with 'objective function value'? If it is different, how we define the score? I use single objective GA run using Matlab optimization tool. I only have bounds for my variables. Thank you

Although the genetic algorithm is very helpful to find a good solution, it is very time-consuming. I've recently read an article in which the authors used the genetic algorithm for pruning a neural network on a very small dataset. They pruned a third of their network and reduces the size of the model from 90MG to 60MG. Their pruning also decreased the inference time of their model by around 0.5 seconds. Although, unlike other methods, pruning using the genetic algorithm does not deteriorate their model's performance, finding redundant parts of the network using the genetic algorithm is an overhead. I wonder why they think their method is useful and why people still use the genetic algorithm.

I want to use pyomo to model MILP problems in python such as Green Vehicle Routing Problem and others but use metaheuristics algorithms (GA, Harmony search, etc.) to solve those problems instead of using pyomo solvers, its part of my phd project and can not use derivative-based solvers at all; If the answer that I can not, what is the alternative to model the problems in python then solve them with metaheuristics algorithms because i found pyomo easy and time saving to be used in formulating the problems.

Hi,

How can we prove the results of the GA that are optimised, in other words, are there criteria or measures to determine the best results (optimize results).

regards

MATLAB optimization tool box. nothing mentioned in the matlab documentation.

Need to know the procedure of implementation as various options are available in CST for optimization.

I have studied in research paper adaptive genetic algorithm, adaptive firefly algorithm but the clear description is not available in those research papers. Plz help me so that I can clarify this and why it is used ?

Is there an index that includes the

**mixing index**and**pressure drop**for**micromixers?**For example, for heat exchangers and heat sinks, there is an index that includes heat transfer performance and hydraulic performance, which is presented below:

**η**

*=(Nu/Nu*_{b})/(f/f_{b})^{1/3}The purpose of these indexes is to create a general criterion to check the devices' overall performance and investigate the parameters' impact.

Is there an index that includes

**mixing performance**and**hydraulic performance**for**micromixers?**Hello,

When I should use memetic algorithm rather than genetic algorithm?

Best Regards,

How does one optimize a set of data which is comprised of 3 input variables and 1 output variable (numerical variables) using a Genetic Algorithm? and also how can I create a fitness function? How is a population selected in this form of data? What will the GA result look like, will it be in a form of 3 inputs and 1 output?

I do understand how the GA works, however, I am confused about how to execute it with the form of data that I have.

My data is structured as follows, just for better understanding:

3 columns of Input data, and the fourth column of Output Data. (4 Columns and 31 rows) data. The 3 input variables are used to predict the fourth variable. I want to use GA to improve the prediction results.

Lastly, Can I use decimal numbers (E.g. 0.23 or 24.45); or I should always use whole numbers for chromosomes.

I have running GA optimization using "gamultiobj" in Matlab. The upper bound and lower bound of my design variables are like [10 30 175 1] and [30 60 225 3]. But after convergence, I see the design variables (like all of them) near 20,47,175 and 1. I am not getting a Pareto Front. What could be the possible reasons for that?

how genetic algorithm fittness function calculated for botnet attack detection?

I already saw some examples of GA (genetic algorithm) applications to tune PID parameters, but I (until now) don't know a way to define the bounds. The bounds are always presented in manuscripts, but they appear without great explanations. I suspect that they are obtained in empirical methods.

Could Anyone recommend me research?

Assuming, I have a number of vectors where each item in the vector is a real number. I was to generate some vectors from those initials ones and add them to the collection of initial vectors. This was I can have a initial population of increased size. What are some ways to do this?

Dear all,

according to [1], QGA are more efficient than GA (genetic algorithms), and according to [2] PSO is better than GA. Do you know if there are papers that do the comparison between PSO and QGA ?

Thank you

[1] - Z.Laboudi (2012) - Comparison of Genetic Algorithm and Quantum Genetic Algorithm

[2] - F D Wihartiko (2017) - Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus

time tabling problem

How can I optimize ANFIS using Genetic Algorithm; and also with Aquila Optimizer in MATLAB? Any available code I can use?

Dear Members

I had recently gone through a sample program code (in MATLAB) corresponding to

**Pareto GA**in Appendix II (Program 4, Page: 219) of the book**. However, I failed to understand the***PRACTICAL GENETIC ALGORITHMS*by Haupt**Niching strategy**employed in the program code. Also, kindly explain the**Pareto dominance tournaments**that have been employed or used for mate selection in the example provided in the book. The flow diagram for Pareto GA delineated in Figure 5.2 of the book, mentions an increase in the cost of closely located chromosomes. But the code apparently contains no such steps. Kindly explain the anomaly.hello!

how i can use the Neural network fitness function i.e., after simulation the value we get basically in terms of MSE(mean squared error). so can you tell me how we this MSE as fitness function in Genetic Algorithm in order to minimize the MSE for better neural netwrok model.

hello!

can anyone tell me how i can integrate the neural network and genetic algorithm. i have a simple real coded genetic algorith where i need to use the neural network. please help me on thia. my code is in matlab. if some one provide me the code

How can I get a MATLAB code for solving multi objective transportation problem and traveling sales man problem?

Here, I have implemented my genetic algorithm with combining two crossover operators i.e..(PMX,OX). After running the algorithm I am getting same fitness values as output from staring generation to ending generation. There is no change Is it correct.? please can anyone answer

Best regards

Anna

Hii,

I have implemented genetic algorithm with partially mapped crossover for solving tsp. Here I'm using different random.seed vlaues(24,42,72,112). When I run the algorithm , can I get the same graph with different seed values without changing the parameters but city co-ordinates are randomly generated.

Please can anyone answer..

best regards

Annapareddy

Hello all, I am new to image processing.... I want to detect edges from an image. When I searched on detecting edges in images I came across about edge detection using genetic algorithms. Where I find the chromosomes and find fitness function for crossover and mutation process. Also, they say that chromosomes are binary array. I am finding it difficult to understand how can we find binary arrays from an image and picking that as a chromosomes or do we have any methods to find chromosomes from an image. Please help me...any help is appreciated.

Hii,

I have implemented genetic algorithm with partially mapped and ordered crossover for solving tsp.

In partially mapped crossover, I have taken 100 generations, when I run the algorithm the best solution is getting of 20th generation. from 20th generations onwards the solution is constant until last generation( 100). may I know the error . Please acn anyone suggest the error.?

Thank you

Define attributes suitable for comparison of both algorithms?

Genetic algorithm with tabu search OR Genetic algorithm with simulated annealing ?

I am using an evolutionary-based GA optimisation tool that divides the space of possibilities into subsets to perform hypercube sampling within each subset, and thereafter generates multiple generation of results, the best of which will form the Pareto front and through each iteration move closer to the global optimum. However is it possible that through the process of hypercube sampling (and hence disregarding some options) , this tool might miss a true global optimum?

While working in both the software, after loading the training and validation data for the prediction of a single output using several input variables (say 10), it skips some of the inputs and delivered an explicit mathematical equation for future prediction of the specific parameter but it skips some of the input variables (say 2 or 3 or maybe greater). What criteria are these software uses in the back for picking the most influential parameters while providing a mathematical predictive model?

Hello everyone,

Could you recommend papers, books or websites about mathematical foundations of artificial intelligence?

Thank you for your attention and valuable support.

Regards,

Cecilia-Irene Loeza-Mejía

Both genetic algorithms and metaheuristic algorithms are optimization algorithms. Is one category of these two is included under the other?

I am working on RCSR. I need genetic algorithm for 1-bit coding Metasurface so that i can arrange my unit cells.

in his name is the judge

hi

I wrot a sub code on opensees for active tlcd or tuned liqiud gass damper (tlcgd) and assign it to some structures, it seems worked correctly.

In next step i want to optimize tlcgds location on each story with some objects like max dislacement or torsion ratio and ... so i have to use multi objective optimization (which may NSGAII algorithm is the best choice) code or toolbox on matlab and simulink. For this purpose i want to run NSGAII algorithm in matlab, then algorithm Calling my code in opensees (tcl) and run it, after that NSGAII algorithm modify damper location (in opensees code) after each timehistory analysis In order to improve objectives and then analysis my code again and again until find best location for dampers.

Note that I actually want to changing dampers location be part of the NSGAII algorithm and the algorithm itself automatically relocation the dampers to get the best answer.

one best solution may use openseespy but i think it's not free access and i can't get it from iran, So i think realyhead Over heels in this case.

Any help is greatly appreciated.

Take refuge in the right.

I am studying on global optimization algorithm now, it seems that there are many different versions of each kind of algorithm. Only for particle swarm optimization (PSO) algorithm, there are many versions like APSO, CPSO, PSOPC, FAPSO, ARPSO, DGHPSOGS (see Han and Liu, 2014, Neurocomputing). In addition, the class of genetic algorithm (GA), differential evolution (ED), ant colony optimization (ACO), simulated annealing (SA) and so on, can also be used to solve the problem. When we develop a new global algorithm, it is worthwhile comparing the performance of these different methods on some benchmark functions (like Resenbrock function and Rastrigin function) in a fair way. For example, I would say the average number of evaluations of cost functions, the success rate, and the average time-consuming is good as measurements for comparison.

So my question is "is there any source code (Matlab) has been developed for comparing different kinds of global optimization (GO) methods?". The code should be easy to use, convenient to fairly compare enough advanced GO methods, and should also have provided enough benchmark functions (given gradient of each function would be better, so that we can compare some gradient-based global optimization algorithm).

I am looking forward to your answer and greatly appreciate your help.

Best wishes,

Bin She.

I am working on a hybrid wind turbine system and pumped storage system to meet the load of Morocco. the objective is to optimize the gain to resume as much as possible from #pload. optimization #PSO #genetic #algorithm #using #python.

the objective function to be maximized :

I wanna optimize 2 complex functions (that have an imaginary part and a real part) using MOGA in Matlab, Are there any other methods for this type or there is no change even if the objective functions are complex?

NB: variables are all scalars

please I need help, thanks in advance

I am presently working on the application of the genetic algorithm for solving multi-objective optimisation of machining parameters.

I need assistance in this area. especially as it has to do with genetic Algorithms.

Hi,

Can anyone be kind to help me with optimizing PID controller gains using genetic algorithm for a MIMO system (Twin Rotor MIMO System (TRMS)). I have been stuck with this problem for quite a very long time now. I have uploaded the code and simulink model for the TRMS with the decoupler that i used.

Whenever i call and run the optimtool for the ga program in MATLAB, the tool does not optimize the system. Instead the system fails to decay and settle on the step input, but both the pitch and yaw signals become unstable and uncontrollable (go into infinity along the y-axis).

I believe the code i am using is wrong and maybe the configured simulink model. Somethings are wrong.

Please I need help Sirs. Thank you

I have a multi-objective optimization with the following properties:

Objective function: thee minimization objective functions) two non-linear functions and one linear function

Decision variable: two real variables (Bounded)

Constraint: three linear constraint (two bounding constraint and one relationship constraint)

Problem type: non-convex

Solution required: Global optimum

I have used two heuristic algorithms to solve the problem NSGA-II and NSGA-III.

I have performed NSGA-II and NSGA-III for the following instances (population size, number of generations, maximum number of functional evaluations(i.e. pop size x no. of gen)): (100,10,1000), (100,50,5000),(100,100,10000), (500, 10, 1000), (500, 50, 25000), and (500,100,50000).

My observations:

Hypervolume increases with increase in number of functional evaluations. However, for a given population size, as the number of generation increases the hypervolume reduces. Which I think should rather increase. Why am I getting such an answer?

I want to optimize an objective function (power plant problem) in MATLAB, connecting with EES. The function file in MATLAB runs without issues, but when the genetic algorithm file is run, I keep getting the error statement and the operation terminates.

I need the recent resources( videos and websites) and reseach papers in term of multi processor scheduling using ant colony with genetic algorithm with python code description