An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.
Key Terms in this Chapter
Magneto-Rheological Fluid: A smart material for engineering applications. Its physical and mechanical properties can be tailor-made by adjusting a magnetic field.
Non-Calculus-Type Algorithms: The optimization algorithms that do not require any derivatives or gradient information. Commonly used to describe genetic and evolutionary algorithms.
Yankee: A device used in the paper mills which essentially is a very large heated cylinder with a highly polished surface.
Classical Techniques: Multi-objective optimization routines that are not evolutionary in nature. Most are based upon rigid mathematical principles and are commonly gradient based.
Superalloy: A specially designed category of multi-component alloys used in advanced engineering applications.
Crown: A quantifier for the surface flatness of the rolled metal sheets.
Guillotine Cutting: Edge-to-edge cutting of materials, most commonly metals.
Pareto-Optimality: The concept of the family of optimized solutions for a multi-objective problem proposed by Vilfredo Pareto (1848-1923 AU17: The in-text citation "Vilfredo Pareto (1848-1923" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Continuous Stirred Tank Reactor (CSTR): A specially designed agitated vessel used for carrying out chemical reactions.
William and Otto Chemical Plant: A hypothetical chemical plant used as a common test problem in chemical engineering literature.