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 optmization etc) to solve multi objective optimization problems.
Here in this example a famous evolutionary algorithm, NSGA-II is used to solve two multi-objective optmization problems. Both problems have a continuous decision variable space while the objective space may or may not be continuous. The first example, MOP1, has two objective functions and six decision variables, while the second example, MOP2, has three objective functions and twelve decision variables.
nsga_2.m is the main function (infact it is mainly a script). Kindly read the accompinied pdf file and also published M-files.
Since there has been a lot of interest in evolutionary algorithms, I am sharing my homework files from last semester. Feel free to edit them according to your needs and feel free to post your comments/suggestion/critisim. I am more than happy to hear from your.
· MATLAB Release: R14SP1