Hello there!
Is there any recommended metaheuristics for solving many-objective constrained optimization problems?
Thanks
Hi,
First: It is not at all necessarily a good idea to use any metaheuristic at all for your type of problem. It all depends on the properties of the variables, the feasible set, and the objectives that you have. Pareto optimal solutions can be obtained in many cases with the use of classic optimization methods for multi-objective optimization problems.
So before you decide that you MUST use a metaheuristic for the problem you want to solve, you SHOULD also look at proper, convergent methods in multi-objective optimization.
Do you have any special properties of your problem that you can share? How many objectives do you have? How are variables defined, and the feasible set?
The web page
http://users.jyu.fi/~miettine/engl.html#software
has links to some free software; try them out!
Additionally to the right advices of Michael Patriksson I can say that practically all the heuristics can solve constrained or not constrained multiple objectives optimization tasks. With some of them you can reach higher effectiveness that with others, that depends on each concrete case. . I am attaching 2 works: the first one describes the Integration of Variables method, that contains all heuristics or metaheurístics that are characterized by making evolve solution populations codes and the other one proposes an Analysis and Synthesis methodology of Engineering Systems that can help you to appropriately formulate your problem and contributes an important number of solutions to multiobjetive optimization tasks helped by the Integration of Variables method.
Thanks for the answers.
Indeed, the problem comprises 8 objectives and three nonlinear and non-convex constraints, whereas the variables are mixed (real and binary).
I insist that the problem you have could be solved without difficulties applying some of the algorithms of the Integration of Variables method to the appropriate Tchebyshev Program (general multiple criteria model)
Among various methods, genetic algorithm approach and /or Integration of variation method might be appropriate to tackle the problem.
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