Multi-Objective Optimization using Evolutionary 1.0

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Program licensingScript Licensing : BSD - BSD License
CreatedCreated : Sep 6, 2007
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Conventional optimization algorithms using linear and ...

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_algorithm (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.
Demands:
• MATLAB Release: R14SP1

Multi-Objective Optimization using Evolutionary 1.0 scripting tags: algorithms, optimization, evolutionary, multi-objective, multi-objective optimization, evolutionary algorithm, problems, matlab miscellaneous, decision. What is new in Multi-Objective Optimization using Evolutionary 1.0 software script? - Unable to find Multi-Objective Optimization using Evolutionary 1.0 news. What is improvements are expecting? Newly-made Multi-Objective Optimization using Evolutionary 1.1 will be downloaded from here. You may download directly. Please write the reviews of the Multi-Objective Optimization using Evolutionary. License limitations are unspecified.