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I'm looking for Matlab code for optimal capacitor placement/sizing using genetic algorithm.
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I need to know the most recent Artificial Intelligent (AI) and Evolutionary Algorithm (EA) Optimization methods to solve Optimal Power Flow Optimization problem.
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Good answer by Hüseyin Bakir
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I am using Optimizations techniques for my research work on demand response. To deal with uncertain parameters and variables stochastic and robust optimization are used. I wanted to learn these techniques and subsequently implement in my research work. What are the good books and optimization softwares to start with?
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See Chapter 12 in:
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I am interested in  the optimal weight design of a gear train problem using optimization methods. There are a variety of studies on this topic.  One of them is "A Solution Method for Optimal Weight Design Problem of the Gear Using Genetic Algorithms" by Takao YOKOTA et al. The objective funticon and constraints are described as same formulations in not only the study of Yokota et al. but also the later studies. Nevertheless, I cannot find any detail about two terms. The former one is "b5" mentioned in the constraint "g5".  g5 is defined as the distance between the axes. The latter one is "l" mentioned in the objective function. "l" is defined as length of boss.
Could you inform me about these terms. Do the terms stand for a formulation or a constant value. 
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Dear Murat Dörterler, Greetings!
This systematic approach gives the minimum volume of gear.
But you have to read in Russian.
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Please Can someone provide me a Matlab code(GA ,PSO, DE) to  solve hydrothermal schedule problems?
I used to solve with GAMS , now I want to use Matlab
Thank.....
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Almost all the optimization algorithms considers Function Evaluations to compare performance among various algorithms.
Do Function Evaluations number is the most important criteria? If yes/no why?
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Function evaluations are one of the most important criteria for comparison along with statistical analysis such as standard deviation, Friedmann's Test, Wilcoxon(due to the stochastic nature of metaheuristic), minimum fitness as well as average fitness. Function evaluation is important since several metaheurisitics have 2 or more stages in their optimization process (for example TLBO has teacher and student phase, GWO has only phase (searching and hunting), ABC also has two loops, Jaya has one , BFO has 5 etc). For all of them number of iterations may be same but that does not reflect the true computational requirement of the algorithm in terms of how much effort was required by the algorithm. Therefore, by using functional evaluations, a more fruitful comparison can be drawn since we are aiming for a common ground, basically the number of times the fitness function was called.
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I have formulated optimization problem for building, where cost concerns with energy consumption and constraints are related to hardware limits and model of building. To solve this formulation, I need to know if problem is convex or non-convex, to select appropriate tool to solve the same. 
Thanks a million in advance. 
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The loss function used in ListMLE or ListNet are smooth and differentiable thus optimization of loss function is easier as compared to pairwise LTR algorithm. But is any increase in performance occur due to reducing the features in ListNet and ListMLE ?
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There is no neural network technique that has no variance except if you want under-fitting model. Please, read about bias-variance trade-off. This problem caused by sampling methods in each learning iteration. Also, please check this link for over-fitting and under-fitting problem
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My question is how to relate my fitness function equation with my parameters that i want to optimize when writing the code in matlab please i need a help?
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Hello sir,the information is not in detail may be if you comes back you can send me via my email adamu.jabire@tsuniversity.edu.ng.
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Now I am working on the blast furnace iron-making process modelling. There are a large number of available process variables possibly related to the quality characteristics of hot metal (pig iron). However, some process variables may be not only garbage but also sometimes degrade the performance of a predictor designed on the basis of a finite number of training samples. In such a case, we need to find an efficient variable selection algorithm to select the useful variables from the candidate variable set for improving the performance of a predictor.
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O(∩_∩)O,please explain it in detail, professor Pan.
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On the Anfis editor gui, there are two default optimization methods namely "hybrid" and "backprop". However, I want to use the conjugate gradient descent method to do my optimization but I do not have any idea on how to have it as an option in the optimization method dropdown on the Anfis editor interface in matlab.
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Hi, Anfis seems a closed tool and thus you can only use the optimization algorithms proposed in the GUI. To use another algorithm you should rewrite your code and call directly another MATLAB solver.
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Dear Sir,
I am student of B.tech electrical engineering of Nirma University in final semester. I am doing my major project in which I am developing the code for BLDC motor design using particle swarm optimization.
I am making single objective multi-variable code for motor design, in this i am facing issue in calling multi-variable function. Can you show me the syntax of calling multi-variable function in PSO initialization?
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You may follow Mahamad Nabab Alam and read his paper and algorithm as in
Codes in MATLAB for Particle Swarm Optimization
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I need a case study of an energy hub system with multiobjective function in order to well understand the concept of energy hub with multicriteria.
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Generally speaking, multi-objective optimization often deals with objectives with contradictory interests, where you may want to minimize the trade-off among the objectives. As far as energy hub system is concerned, you need to specify the goals of the optimization; in one case, economic optimization is what may arouse interests, where there are many high-quality contributions on Unit Commitment and Economic Load Dispatch; another scenario could be power-flow optimization within the network. In some cases, these two aforementioned objectives coincide.  
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Am working on Antenna design using pso but driving the fitness function for optimizing the width of my antenna is giving me proble. Please how can i formulate my fitness function ?
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  • You can check the code of Circular Antenna Array Design Problem in CEC2011. 
  • I have attached the report, you can check the link to obtain the MATLAB codes.
  • Based on that code and your problem, you can revise your objective function.
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Dear Dr. Skender Kabashi,
Does this project involve HVAC Optimization and control as well besides the GHG and airpollution?
Regards, Junaid 
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in my current work i employ the GA optimisation method in matlab to search for the best solution, hwoever i find that the GA method easily converges to local optimum but not global one, especially when this local optimum is very close to global optimum, could you help to explain how this happend, and how i can improve the GA method to avoid converging to the local optimum? thank you!!  
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Hi,
one of the solutions to escape local optimum is to perturb your new GA generation by adding some random chromosomes at the end of each new generation. Note that the randomness that you add must be well chosen and small compared to the rest of the genetic operators otherwise, you will have a random search instead of guided GA search.
Another solution is to reconsider the mutation rate (which induces some randomness to your population), be careful though with the chosen rate otherwise, your GA will be too perturbed to find a good solution. You can also consider allowing some bad solution to pass to the next generation, the same remarque here, the number of bad individuals that you allow to survive must not be big just a little few otherwise your GA will not converge to the hopeful results
I hope that this answer is helpful 
Good Luck
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Any body having partical swarm optimization alogrithum implemented in MATLAB for multi objective, multi constraint problem.
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Dear Faizan,
I suggest to you links and attached files in topics.
-Multi-Objective Particle Swarm Optimization (MOPSO) - File Exchange ...
-Yarpiz - MATLAB Central - MathWorks
-Multi-Objective PSO in MATLAB - Yarpiz
yarpiz.com › Multiobjective Optimization
-NSGA-II in MATLAB - Yarpiz
yarpiz.com › Multiobjective Optimization
- Particle Swarm Optimization and Intelligence: Advances and ...
 Best regards
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Hi all, i am working on global optimization solution to economic load dispatch algorithms-particularly multi-objective optimization. could you please provide the coding for ant-lion optimization algorithm?
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Thank you so much for your suggestion sir... 
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How can we leverage the lossless convexication technique to convexify them
beforehand? I can't do this work for my own question by referring to Ackmese and Blackmore's paper according to the paper 'Successive Convexication of Non-Convex Optimal Control Problems with State Constraints' said, can you help me with it?
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Dear James F Peters ,
Thanks for your reply, and I have read the paper you provided. And I have solved the convex optimal control problems with high efficiency,but I can't convert the nonconvex optimal control problems as the attached file drawed to convex, can you show me the way to the problem? If you can, can you show me the result?
Yours,
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I am working on this project currently and I am not able to find any flowchart/algorithm for Dial a ride problem in order to code it in Matlab.Please do share any thesis papers/literature review if you have any.
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Why don't you try to evolve the sequence of operators. If you are interested  contacted me.
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I want to modify Clarke and wright saving algorithm so i can afford heterogeneous fleet. In my current project I implement the saving algorithm in c#  to solve asymmetric capacitated vehicle routing problem and GMap.NET to visualize the route. But the saving algorithm only afford homogeneous fleet so I search in many literature. After many hours searching, I can't find suitable information. So is there any reference that explain about heterogeneous fleet in Clarke and Wright saving algorithm ?.
Note : this is my first question and I'm interested in vrp problem but I don't know where to ask. Sory about my english grammar.
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1. Recent or existing optimisation techniques which is applied to optimisation of thermal systems.
2. Recent optimisation tools used in thermal engineering applications.
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Dear all,
I am working on size optimization of frame structure. And I am using Design of planar steel frames using Teaching–Learning Based Optimization as a base paper which is based on  the American Institute for Steel Construction (AISC) Load and Resistance Factor Design (LRFD).
Here I faced difficulty to find density of material. For that I refereed similar typed of paper but density is not specified.
Can anyone help me to get density of it.
Thanks 
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you can contact with the authors, but you can solve problem with different values to obtain the unknown parameter.
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Dear all,
I am working on Modification of meta heuristics. I need to perform Friedman Rank Test to check statistical test of CEC 2014.
Here, I have best, mean, and standard deviations of all meta heuristics for 60 runs. The sample file 'test 1' shows sample results of CEC2014 functions 1 and 2 for 6 runs.
Please share your view about this test.
Thanks
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you can check this link, there are also other similar codes that you can find:
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Dear all,
I am working on optimization algorithms for their efficiency/inefficiency. These algorithms involve:
1. Gradient based methods: Variable Metric method, BFGS.
2. Derivative Free Methods: Hooke and Jeeves Method, Nelder-Mead Method, Multi-directional Simplex Method of V. Torczon, Frame based method of CJ Price.
3. Meta-heuristics: PSO, ABC, DE, FA, WCA, TLBO, CMA-ES
I need some suggestions in choosing some real word optimization problems for the applications of the modified approaches of the above algorithms.
Regards
Javaid Ali 
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Dear Javaid,
you choice will depend on the characteristics of your problem.
For example derivative-free methods like Nelder-Mead simplex method usually give good results when the number of unknowns is small (less than 10 in general).
On the other side, gradient based methods require the cost function to be differentiable. It is not clear this property is satisfied in your real world problem.
Meta-heuristics are perhaps more adapted but you will have to try different tuning of the method in order to compare the results of the different runs. You will not be sure to converge to a local optimum each time.
In addition in your case does efficiency mean "efficiency in converging to a local minimum" of does it mean "efficiency in converging to a low value of the cost function (not necessary a local optimum) in a short CPU time" ?
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please refer a book that deals with the detailed exploration and exploitation of metaheuristics of recent time.
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I would recommend you to read a very interesting journal paper 
Metaheuristics—the metaphor exposed
Authors
Kenneth Sörensen
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Dear sir,
I want to work on the modified PID controller  and compare with conventional PID controller and tune by using optimization algorithm, then can I change the limits bound for MPID controller for every gain in comparison of conventional controller?
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Hi Ankush,
You should compare both robustness and performance of the two controller. For this, a pareto optimalcity curve can be used. Look at the enclosed paper to get an idea how to define the cost functions.
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According to the algo "the initial soil on each path (edge) is denoted by the constant InitSoil such that the soil of the path between every two nodes i and j is set by soil(i, j) = InitSoil."
If the soil is same at every edge, won't the probability of a water drop to move from the ith node to any other node be same?
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Dear all,
Please share your view and relevant materials for symbolic regression in optimization methods.
Thanks
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I would look at the latest paper by Nuno Lourenco.
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I want to perform cycle studies on an existing system. So I need to modify some existing pipes to control the temperature.
Do you know if heater and cooler in one piece jacket exists ? Like something containing heating wires and cooling Peltier elements ?
Or if you have any suggestion, I'm listening
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The range of temperature will be  like 5-95C ...
Actually, it's an adsorption column (20*2 cm) with a lot of sensor on and in it.. So I don't think I will be able to get no water leak... So I was looking for something different than putting a big pipe over all the system...
For the heating,l it can be an heating wire but my major concern is about the cooling (to have something better than only natural convection)
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The PCM whose latent heat of fusion is more and must have high cyclicity.
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I would suggest 1-decanol; having the fusion enthalpy of approx. 37.7 kJ/mol (*) at the (normal) fusion temperature of approx. 6.6 ºC.  
(*) J.A. Dean (Ed.), "Lange's Handbook of Chemistry", 15th ed., 1999, McGraw-Hill, p. 6.58.
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ABSOLUTE VALUE OPERATOR LINEARIZATION
I have a nonlinear term in the objective function of my optimization problem as an Absolute Value function like |x-a|.
As far as I know, an Absolute Value operator makes the optimization problems nonlinear (i.e. NLP). How can I make it linear (LP or MILP)?
Max  f(x)=g(x) + b*|x-a|
   s.t.   some linear constraints
Regards,
Morteza Shabanzadeh
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max f(x)=g(x)+b*p+b*q
s.t
other constraints
x-a+p-q=0;
p,q>=0;
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I want to know that how to change the second law thermodynamics efficiency (exergy efficiency) of compressor with respect to compressor ratio?
Is it increases? or decreases?
Do you have any articles in this regard?
thanks for your help,
best,
Ali
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What I mean is : your pressure ratio is not enough if you want to model the behavior of your compressor. You also need to know, or make an hypothesis about, its polytropic exponent [1], so about the way it exchanges heat with the surroundings. For a given value of pressure ratio and polytropic exponent, you can calculate the values of outlet pressure and temperature. From that, you can obtain the value of the specific exergy of air at the inlet and outlet.
Besides this, the polytropic efficiency of your compressor will lead you to the value of the work consumed and then to the exergy efficiency.
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Is Cplex free or not, and how simple it is to use?
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InsyAllah I will explain in detail soon.
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Hello,
I would like to know that how I can find the number of variables (especially the integer ones) in GAMS (General Algebraic Modeling System) codes.
Does GAMS platform have any options to show the number of variables?
Any help would be appreciated.
Regards,
Morteza
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Hi,
If you are working with GAMS IDE (the Integrated development Environment of GAMS that runs in Windows), then you can find the number of variables (and their type) in the log file. The log file is the window that pops up when you run your code. You can look for the number of columns (i.e. the variables) and the number of integer-columns (i.e. the number of integer variables)
That is the easiest way for me to identify the number of variables, but maybe someone else has a better way to do it.
Regards,
Laura
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For some practical problems and objectives can be applied multidimensional optimization with a given distribution of ranked variables be applied?
Distribution of ranked variables of a searching vector should be similar to the vector variables with a given analytical dependence.
For example, a vector similar to a hyperbola.
I have developed a method for multi-objective optimization placement capacitor banks in radial distribution networks to optimize the loss of active energy
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Many thanks for your reply.
I make no statistical computing tasks.
Function has a  nonlinear analytical dependence.
I have the solution to the problem and looking for new applications.
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NONANTICIPATIVITY
As far as I know, decision making under uncertainty can often be formalized as a stochastic problem. I have seen a constraint which is called "Nonanticipativity" in the most papers regarding Stochastic Programming.
I would like to know that
1) What is its concept and role?
2) Is it essential for stochastic optimization problems or just for some especial stochastic problems?
3) Can I ignore it?
I would greatly appreciate any help.
Regards,
Morteza Shabanzadeh
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There are two main approaches to solving multi-stage stochastic programs: Benders decomposition, which decomposes the problem by scenario, and Lagrangian decomposition, which decomposes it by time-stage instead.
The non-anticipativity constraints appear in the latter approach.  Basically, you have two copies of each variable, one representing the value it takes before you know the realisation of some random parameter, and the other representing the value it takes after you know the value.  In reality, the values of the two variables must be equal, since you have to fix the value of the variable before you know the realisation.  So non-anticipativity constraints are just equations stating that two variables must take equal values.
Now,  by relaxing all of the non-anticipativity constraints in Lagrangian fashion, you obtain a relaxation that decomposes into one independent subproblem per time-stage.  This gives a bound for your original problem.  Then, standard techniques (such as the subgradient method) can be used to solve the Lagrangian dual, i.e., find a collection of Lagrangian multipliers that give you the best bound.
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Hello,
As far as I know, the meta-heuristic algorithms such as GA, PSO, GSA, etc. generally find the optimal solution of 'unconstrained' optimization problems. If I have some constrains (equality and/or inequality equations), how will I be able to consider and model them in these kinds of algorithms?
I would greatly appreciate it if you kindly help me in this matter.
Regards,
Morteza Shabanzadeh
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Hi Morteza, I am new to participating in the Q & A. I hope I  have understood your question appropriately and that is that you are asking about "good" methods of constraint handling? If I have got tthe gist of your question, then this is perfect because I am very interested in this topic myself.
I put "good" in  quotation marks above because I am convinced that what is judged to be good is problem dependent, unfortunately/. To me, this is just a manifestation of Wolpert and MacReady's No Free Lunch theorem.
For a recent overview see: Mallipeddi et al (2010): Ensemble of Constraint HandlingTechniques in IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 14 (4).
There are of course a number of other reviews of constrained optimisation techniques available in the literature. Prof  Carlos Coello-Coello's repository found on the web at:http://www.cs.cinvestav.mx/~constraint/index.html is a useful starting point.
Recognising that the definition of "good" method might be problem specific, I give a brief summary of what I have, for my problem setting investigated, found to be efficient. Note that these algorithms can be used with population based stochastic heuristics (like GA/DE/EP/ES/PSO), I am not so sure about using them with Simulated Annealing (SA) as canonical SA operates with a single trial point rather than a population.
Epsilon constraint handling technique as proposed by T. Takahama and S. Sakai, iin their paper "Constrained Optimization by the " Constrained Differential Evolution with an Archive and Gradient-Based Mutation", pp.1680-1688. (Winner of the CEC 2010 competition on constrained single level optimisation)
The Stochastic Ranking Method of Runnarsson and Yao with code in MATLAB and c: https://notendur.hi.is/tpr/index.php?page=software/sres/sres It is easy to get the wrong idea that it only works in Evolutionary Programming (Prof Runarsson and Yao's EA of choice but I have managed to use it as a constraint handling method with Differential Evolution.)
Interestingly one of the simplest and most effective method which I have tried is the method given in Kim et al (2010): T-H Kim, I. Maruta and T. Sugie. A simple and efficient constrained particle swarm optimization and its application to engineering design problems, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, Vol. 224, No. C2, pp. 389-400, 2010.
See particularly Equation 2 in this paper. It should be easy to program these in any language of your choice.
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Several current optimization techniques based on evolutionary evolution try to mimic the social behavior of living communities: fishes, birds, ants, bees and even the human being. It is sometimes mentioned the existence of a “social intelligence”. Are the bases of such techniques always consistent? The resulting algorithms can work better than the supporting bases? There is not the risk of introduce or increasing the empiricist component of the approach?  
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Dear Antonio, I suggest you to avoid the metaphors and concentrate on the basics of the techniques.
Perhaps you may find the following article interesting
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I want to find the force/pressure of expanding mandrel of collet to optimize its parameters.
Please suggest, how to analyze this pressure using MATLAB or ANSYS or CREO.
Further I want to interlink these results with MATLAB to optimize design parameters for minimization of force/pressure of expanding mandrel.
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First you need to specify the grip force. From that you will specify collet’s outer diameter and length. Some factors affect the grip force:
D (see photo): high axial forces will result in higher gripping forces
Angle : defines the force amplification.
Friction : depends on material selection, heat treatment, surface roughness and lubrication.
To arrive to optimum gripping force you should consider the followings:
1) Machining force
2) Good ratio of amplification
3) Collet should be capable to rapidly release the workpiece. To this end self,ocking angle should be avoided.
4) Centrifugal rotational force should be considered, etc.
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Assume,
A=[1 2 3 4]
and I need all probable connection of any two number
like
Answer = [1 2; 1 3;1 4;2 3;2 4;3 4]
or for 3 connections
Answer =[1 2 3;1 2 4; 1 2 4; 2 3 4]
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If you just want the number of combinations, you can use
m=nchoosek(n,k)
or you can do
m=factorial(n)/(factorial(n-k)*factorial(k))
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I need a multi-objective optimization algorithm
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Sorry, and apologies in case my answer may sound a bit too tough : The idea "to convert a single objective optimization algorithm to a multi-objective one" only shows a lack of understanding of the essence of the multi-objective optimization. Indeed, it is just about as off the point, as trying, for instance, to convert in general partial differential equations into ordinary differential equations. Of course, one can from the start simplify grossly and brutally the multi-objective nature of the given problem, and reduce it to one single objective. However, the real and truly valuable practical issue is to avoid that, and instead, to keep the multi-objective situation alive all the time, that is, throughout the whole process of solving of the optimization problem. Now, since the late 1970s, it is known that in a multi-objective context, the role of preference type information is fast diminishing with the increase in the number of objectives. Therefore, instead of preference type information, one is obliged to use other information, such as for instance, indifference information, that is, to what extent one is indifferent between two possible outcomes. Details in this regard can be found, for instance, at arxiv:math/0506619
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I have a single objective optimization, for example GWO, and I need a multi-objective algorithm. Then I want to convert it to multi objective.
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Sorry, and apologies in case my answer may sound a bit too tough :
The idea "to convert a single objective optimization algorithm to a multi-objective one" only shows a lack of understanding of the essence of the multi-objective optimization. Indeed, it is just about as off the point, as trying, for instance, to convert in general partial differential equations into ordinary differential equations.
Of course, one can from the start simplify grossly and brutally the multi-objective nature of the given problem, and reduce it to one single objective.
However, the real and truly valuable practical issue is to avoid that, and instead, to keep the multi-objective situation alive all the time, that is, throughout the whole process of solving of the optimization problem.
Now, since the late 1970s, it is known that in a multi-objective context, the role of preference type information is fast diminishing with the increase in the number of objectives. Therefore, instead of preference type information, one is obliged to use other information, such as for instance, indifference information, that is, to what extent one is indifferent between two possible outcomes.
Details in this regard can be found, for instance, at arxiv:math/0506619
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There is positive definiteness of the stiffness matrix of truss, and this can be checked by positive Eigenvalues. But I'm not getting all Eigenvalues as positive.
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By the way, your truss looks odd to me --- there is not a rod linking nodes 2 and 3 or linking nodes 4 and 5. I suggest that you caculate
max(eigenvalue(stiffness(activeDof,activeDof))) / min(eigenvalue(stiffness(activeDof,activeDof)))
and let me know what it is.
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There are many areas in electrical as well electronics engineering where we can apply optimization to minimize objective function. For example to minimize Total Harmonic Distortion (THD) in multilevel Inverter researcher had applied optimization Technique (like GA,PSO etc).
In this forum researcher (specially electrical & electronics field) will get a overall idea where he/she can apply his optimization knowledge and give his contribution to existing system more improve in future.
Please share your own experience where you have applied optimization program.
Thanks for sharing your knowledge on this forum in advance.
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Although "optimization methods" have wide application in vast areas of Engg. including EEE, in practice, optimizations find to be less valuable. Because,many times optimization deals with a few parameters of the system and neglects many other. Also mathematical modeling of systems like power grids, DGs, Inverters with inductive loads are quite difficult and highly nonlinear. Exhaustive search methods like GA, SA and PSO also find less attraction in practice although many journals or conference papers are publishing many researches in simulation studies.
In many practical cases the benefits we acquire through optimization are less efficient when compared to the effort required to develop the same in practice.
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There are many Optimization method /Evolutionary algorithms (EAs) in literature. Some of them is more effective (for solving linear/non linear problem) compared to other. Algorithm References Inventor&Year 1. Ant colony optimization (ACO) ; I Dorigo and Stutzle (2004) 2. Artificial immune system optimization; Cutello and Nicosia (2002) 3. Bacterial foraging optimization ; Kim, Abraham and Cho (2007) 4. Bee optimization ; Karaboga and Bosturk (2007) Pham et al (2006) 5. Cuckoo algorithm ; Yang and Deb (2009, 2010) 6. Differential evolution (DE) ; Storn and Price (1995, 1997) 7. Firefly optimization ; Yang (2010) 8. Fish optimization ; Huang and Zhou (2008) 9.Genetic algorithms (GA) ; Haupt and Haupt (2004) 10.Particle swarm optimization (PSO), Binary Particle Swarm Optimization (BPSO); Eberhart and Kennedy (1995) 11.Raindrop optimization ; Shah-Hosseini (2009) 12.Simulated annealing ; Kirkpatrick, Gelatt and Vecchi (1983) 13.Biogeography-based optimization (BBO), 14. Chemical reaction optimization (CRO) 15. A group search optimizer (GSO), 16. Imperialist algorithm 17. Swine flow Optimization Algorithm. 18. Teaching Learning Based Optimization(TLBO) 19. Bayesian Optimization Algorithms (BOA) 20. Population-based incremental learning (PBIL) 21. Evolution strategy with covariance matrix adaptation (CMA-ES) 22. Charged system search Optimization Algorithm 23. Continuous scatter search (CSS) Optimization Algorithm 24. Tabu search Continuous Optimization 25. Evolutionary programming 26. League championship algorithm 27. Harmony search Optimization algorithm 28. Gravitational search algorithm Optimization 29. Evolution strategies Optimization 30. Firework algorithm, Ying Tan, 2010 31. Big-bang big-crunch Optimization algorithm, OK Erol, 2006 32. Artificial bee colony optimization (ABC), Karaboga,2005 33. Backtracking Search Optimization algorithm (BSA) 34. Differential Search Algorithm (DSA) (A modernized particle swarm optimization algorithm) 35. Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) 36. Multi-objective bat algorithm (MOBA) Binary Bat Algorithm (BBA) 37. Flower Pollination Algorithm 38. The Wind Driven Optimization (WDO) algorithm 39. Grey Wolf Optimizer (GWO) 40. Generative Algorithms 41. Hybrid Differential Evolution Algorithm With Adaptive Crossover Mechanism 42.Lloyd's Algorithm 43.One Rank Cuckoo Search (ORCS) algorithm: An improved cuckoo search optimization algorithm 44. Huffman Algorithm 45. Active-Set Algorithm (ASA) 46. Random Search Algorithm 47. Alternating Conditional Expectation algorithm (ACE) 48. Normalized Normal Constraint (NNC) algorithm There are many other optimization algorithm recently invented. Some optimization algorithm are combination of two or more (which are generally called Hybrid optimization Technique). Researcher are requested to share their personal experience which algorithm is effective to solve particular objective problem (linear/nonlinear). What are the recently invented optimization algorithm and name of the inventor. Which algorithm requires less time compared to other? Most of the researcher waste time by choosing the right optimization technique for solving their optimization problem.This forum will help a lot for them. I hope Your valuable suggestion will be very helpful to the researcher to choose right optimization method for solving their problems.
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- Population-based incremental learning (PBIL)
- Evolution strategy with covariance matrix adaptation (CMA-ES)
- Charged system search
- Continuous scatter search (CSS)
- Continuous tabu search
- Evolutionary programming
- League championship algorithm
- Harmony search
- Gravitational search algorithm
- Evolution strategies
- Firework algorithm
- Big-bang big-crunch algorithm
- Artificial bee colony optimisation (ABC)
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Plus thousands of their variants. There are also a number of multiobjective versions.
My personal choices for most single objective optimisation problems with continuous DSVs are CMA-ES and DE/2/best/bin.
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I am working on a bridge design project. I would like to know about the optimization software I can use for structural optimization of the bridge?
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Sofistik is very good solution for bridge design. A lot of things can be defined in a parametric manner (e.g. cross section dimensions, bridge axis, position of columns, tendon paths, etc.), either by graphical pre-processor or textually by code, so you can change the parameters very quickly and search for the optimal solution. You can even control the software iterativelly by writing your specific code in its input programming language (kind of intuitive). Good luck
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I am trying to simulate real road traffic at one of the busiest toll plazas during peak time. I'm having difficulty in getting the raw data from the authority. Because of the absence of real time data, I decided to use simulation to give a closest picture of the real situations.
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Dr Benjamin Passow from the DIGITS Group (DMU's Interdisciplinary Group on Intelligent Transport Systems) at De Monfort University (in the UK) just gave a talk on traffic simulation at our university.
It might be worthwhile to get in touch with him:
@Azad: Perhaps a hybrid DES/ABM approach might also be good. The process will be organised by DES while the passive entities (normally used in DES) will be replaced by active ones (agents) that can make and follow their own decisions.
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Who can constitute an optimization equation that includes these variances: circuit breaker position (or recloser, disconnector), its price and minimal power loss in distribution network?
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I can try to constitute optimize equation for you, send details of ur problem. Ur problem is multi-objective problem, u can use weightage approach to solve the problem.
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I am looking for a GAMS source code for solving multi-objective optimization problems.
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Dear Thomas
I would like to thank you for your effort in answering my question.
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Implicit decision variable is the variable internally defining by the constraints.
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In this case you have just one design variable, since x1 and x2 are not independent of each other.. An equally constraint can be used to reduce the design domain.
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Optimum capacitor placement and sizing problem is better solved with software computations.
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0.8 is the best power factor(stability point view).The required capacitor banks can be put on/off foroptimum use by using the method discussed in my paper,"Microprocessor based load sheding controller",insted of load you can controlled the rective power.