Decision-makers in many areas, from industry to engineering and the social sector, face an increasing need to consider multiple, conflicting objectives in their decision processes. Such problems can arise in practically every field of science, engineering and business, and the demand for efficient and reliable solution methods is increasing. The task is challenging because, instead of a single optimal solution, multi-objective optimization results in many solutions with different trade-offs among criteria, also known as Pareto optimal solutions. A multi-objective Genetic Algorithm is a guided random search method suitable for solving problems with multiple objective functions and variables. Solutions of the Multi-objective Genetic Algorithm are illustrated using the Pareto fronts. Academics, industrial scientists, engineers engaged in research & development will find this course invaluable.
This course will teach you to implement multi-objective genetic algorithm-based optimization in the MATLAB environment using the Global Optimization Toolbox. Various kinds of optimization problems are solved in this course. At the end of this course, you will utilize the algorithm to solve your optimization problems. The complete MATLAB programs included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Take advantage of learning and understanding the fast-growing field of evolutionary computation.
Who this course is for:
- Students belonging to all disciplines of Engineering and Science
- Students pursuing projects or research in Optimization
- Working Professionals
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Multi-Objective Optimization Using Genetic Algorithm: MATLAB
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