multi-objective genetic algorithm (moga) approach within matlab scripting Search Results


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The obtained Pareto fronts by using Sigma method <t>,</t> <t>modified</t> NSGAII , MATLAB's Toolbox <t>MOGA,</t> and the proposed algorithm regarding the optimal control design of the biped robot.
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The obtained Pareto fronts by using Sigma method <t>,</t> <t>modified</t> NSGAII , MATLAB's Toolbox <t>MOGA,</t> and the proposed algorithm regarding the optimal control design of the biped robot.
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The obtained Pareto fronts by using Sigma method , modified NSGAII , MATLAB's Toolbox MOGA, and the proposed algorithm regarding the optimal control design of the biped robot.

Journal: The Scientific World Journal

Article Title: Pareto Design of State Feedback Tracking Control of a Biped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAII and MATLAB's Toolbox

doi: 10.1155/2014/303101

Figure Lengend Snippet: The obtained Pareto fronts by using Sigma method , modified NSGAII , MATLAB's Toolbox MOGA, and the proposed algorithm regarding the optimal control design of the biped robot.

Article Snippet: Optimal state feedback tracking control using a multi-objective particle swarm optimization algorithm in comparison to three prominent optimization algorithms, modified NSGAII, Sigma method, and MATLAB's Toolbox MOGA is used here to design the parameters of the proposed controller, while, sliding mode control based upon a particle swarm optimization algorithm is utilized in [ ] to control the biped robot.

Techniques: Modification