Hi Wipul

Hmm, there are probably a number of different toolkits. My favorit is:

http://www.tik.ee.ethz.ch/sop/pisa/

The idea is to separate the problem specific from the algorithm specific.

A major downside is that communication between the two parts is done

through the file system.  This is rather slow, but means that your code does

not need to be in the same language as the general-algorithm code.

The good news is that there are MANY Multi-Objective algorithms:

SPAM - Set Preference Algorithm for Multiobjective Optimization

SHV - Sampling-based HyperVolume-oriented algorithm

SIBEA - Simple Indicator Based Evolutionary Algorithm

HypE - Hypervolume Estimation Algorithm for Multiobjective Optimization

SEMO - Demonstration Program

SEMO2 - Simple Evolutionary Multiobjective Optimizer

FEMO - Fair Evolutionary Multiobjective Optimizer

SPEA2 - Strength Pareto Evolutionary Algorithm 2

NSGA2 - Nondominated Sorting Genetic Algorithm 2

ECEA - Epsilon-Constraint Evolutionary Algorithm

IBEA - Indicator Based Evolutionary Algorithm

MSOPS - Multiple Single Objective Pareto Sampling

EPSMOEA - Epsilon MOEA

You can freely download the whole thing ...

There are many, many different MOEAs to choose from. However, it is encouraged to pick a second generation one (like NSGA-II and the like) due to their properties. For instance, here you can find a MOEA for multiobjective clustering http://grsi.salle.url.edu/alvarog/software/ that makes use of PESA-II. In the reference http://www.sciencedirect.com/science/article/pii/S0951832005002012 you can find a nice tutorial with the state-of-the-art (it's a bit old IMHO, but worth the time) of MOEAs.

I used NSGA II in Matlab from http://www.mathworks.com/matlabcentral/fileexchange/31166-ngpm-a-nsga-ii-program-in-matlab-v1-4. It also have graphical representation.

I too will second Thomas Stidsen's answer as PISA (http://www.tik.ee.ethz.ch/sop/pisa/) is my favorite too.

HyFlex (http://www.hyflex.org, free) is used to generate solutions for difficult problems. It was developed for Hyper-heuristic but I believe can be used in Meta-heuristics too.

I have used in my PhD ECJ (http://cs.gmu.edu/~eclab/projects/ecj/, free), which it is a research Evolutionary Computation (EC) system written in Java. It is limited to the type of Evolutionary algorithms but powerful.

One code which is free on the web is WWW-NIMBUS, written by Kaisa Miettinen. We will actually utilize it in a few weeks in a PhD course that she will give at our department. After that I can provide an assessment of it. :-)

Hi Vipul and everybody,  

There are other options like:

- Parasideo http://paradiseo.gforge.inria.fr/index.php

- jMetal  http://jmetal.sourceforge.net/index.html

-  gplab (only genetic programming) http://gplab.sourceforge.net/

- beagle https://code.google.com/p/beagle/

Have fun ...

Also check these links:

Non-dominated SOrting Genetic Algorithm II (NSGA-II)

http://yarpiz.com/56/ypea120-nsga2

Multi-Objective Particle Swarm Optimization (MOPSO)

http://yarpiz.com/59/ypea121-mopso

Strength Pareto Evolutionary Algorithm 2 (SPEA2)

http://yarpiz.com/74/ypea122-spea2

Pareto Envelop-based Selection Algorithm II (PESA-II)

http://yarpiz.com/86/ypea123-pesa2

Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)

http://yarpiz.com/95/ypea124-moead

The EMOO Repository

http://delta.cs.cinvestav.mx/~ccoello/EMOO/EMOOsoftware.html

Hello try this out:

http://delta.cs.cinvestav.mx/~ccoello/EMOO/EMOOsoftware.html

There is a nice software tool for multi-criteria optimization that uses exhaustive iterative search, ideal for validating meta-heuristic optimization algorithms:

http://www.virtuode.com/index.php/products/brutomizer/

It is very easy to use, and here is a short video of how it is used:

https://www.youtube.com/watch?v=WpGJNda4U5s

Please consider TridentOpt for MultiObjective Optimization. It uses a Hybrid Genetic Algorithm Multi-Agent System to solve problems using fuzzy programming methods. It can be found at

https://www.codeproject.com/Articles/1214046/TridentOpt-A-Multi-Objective-Optimization-Engine-or-Solver

I recommend the algorithm developed by Gilberto Reynoso Meza during his doctorate studies: Matlab source

https://la.mathworks.com/matlabcentral/fileexchange/65145-extended-multi-objective-differential-evolution-with-spherical-pruning-spmodex-algorithm

Here the ResearchGate Profile: https://www.researchgate.net/profile/Gilberto_Reynoso-Meza, where you can find the support papers and applications.

Dear all,

In my opinion, nowadays, the best framework open-source is PlatEMO.

Try it!!!

https://github.com/BIMK/PlatEMO

Article PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective ...

Try out Jmetal (https://github.com/jMetal/jMetal). It has source code and testing data for most of the popular algorithms.

I am using pagmo via Python bindings (pygmo): https://esa.github.io/pagmo2/

the multi-objective extension https://github.com/anriseth/MultiJuMP.jl of Julia's JuMP (https://github.com/JuliaOpt/JuMP.jl) modelling language

you can use MATLAB , GAMS, Spider(Python) or something else. the algorithms you said are slow and inaccurate vs new ones, like :

Article An enhanced time evolutionary optimization for solving engin...

and

Article Hybrid Improved Dolphin Echolocation and Ant Colony Optimiza...

and its better you write your own code , not use a toolkit.

Many thanks for the question and replies, which are all useful. We have the following MS Excel based programs for MOO free of cost. These were developed by my project students as part of our extensive research on MOO and its applications to Chemical Engineering; browse the link: http://cheed.nus.edu.sg/stf/chegpr. These MS Excel programs have been used by many others. Interested readers can contact me through email to [email protected].

You can use the toolbox of MATLAB software, in which has several methods. I use genetic algorithm for problems of multiobjective optimization.

The combination of optimization algorithms included in the Microsoft Excel Solver add-in with Derringer's Desirability function is an alternative to solve multi-response optimization problems without programming. The determination of Hansen's solubility parameters is a good example of this. https://www.hansen-solubility.com/HSP-science/basics.php

PAGMO is your deal.

I highly recommend openGA

https://github.com/Arash-codedev/openGA

It is a user friendly cross-platform genetic algorithm library which supports NSGA-III and parallelism.