|Code Listing by Aravind Seshadri|
Conventional optimization algorithms using linear and non-linear programming sometimes have difficulty in finding the global optima or in case of multi-objective optimization, the pareto front. A lot of research has now been directed towards evolutionary algorithms (genetic algorithm, particle swarm optimization etc) to solve multi objective optimization problems.
Here in this example a famous evolutionary algorithm, NSGA-II is used...
simulatedannealing() is an optimization routine for traveling salesman problem. Any dataset from the TSPLIB can be suitably modified and can be used with this routine. A detailed description about the function is included in...
NSGA-II is a very famous multi-objective optimization algorithm. I submitted an example previously and wanted to make this submission useful to others by creating it as a function. Even though this function is very specific to benchmark problems,...
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