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Optimization is a procedure of finding and comparing feasible solutions until no better
solution can be found. Solutions are termed good or bad in terms of an objective, which
is often the cost of fabrication, amount of harmful gases, efficiency of a process, product
reliability, or other factors A significant portion of research and application in the field
of optimization considers a single objective, although most real-world problems involve
more than one objective. The presence of multiple conflicting objectives (such as
simultaneously minimizing the cost of fabrication and maximizing product reliability)
is natural in many problems and makes the optimization problem interesting to solve.
Since no one solution can be termed as ад optimum solution to multiple conflicting
objectives, the resulting multi-objective optimization problem resorts to a number
of trade-off optimal solutions. Classical optimization methods can at best find one
solution in one simulation run, thereby making those methods inconvenient to solve
multi-objective optimization problems.
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
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Comprehensive coverage of this growing area of research
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Carefully introduces each algorithm with examples and in-depth discussion
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Includes many applications to real-world problems, including engineering design and scheduling
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Includes discussion of advanced topics and future research
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Can be used as a course text or for self-study
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Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. |
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