Application of Genetic Algorithm in Multi-objective Optimization

1. Introduction:
Genetic algorithms (GAs), members of the large class of “Evolutionary Algorithms”, are
metaheuristics approach for solving various optimization problems. GAs are inspired by the natural
selection process. GAs operate on a set of potential solutions applying the principle of survival of
the fittest to produce better and better approximations to a solution. Based on the fitness level of
the individual solution in each generation, a new pool of parents is selected for breeding the next
generation using various operators adopted from natural evolution. Thus at each generation, GAs
try to generate offspring exhibiting better fitness level which are better suited to their environment
than the population they are generated from [1]. In various multi-objective optimization problems,
the applicability of GAs has been proven through numerous research works. Also, there are
competing optimization methods available to solve structural design problems. But certain
characteristics of this class of problems have made GAs popular in this research field. GAs are
suitable for continuous problems as well as for discrete and non-differentiable problems.
Additionally, GAs are very efficient for searching global optimal solutions.
However, there are some interesting areas related to the application of GAs to the structural
optimization problems which are not yet fully explored. The following areas require further
investigation
In constrained optimization problems, application of penalty function is very common. GAs are
successfully used with penalty function applications. However, there is no systematic approach
to understand the influence of the magnitude and trend of penalty function on the convergence
towards the global optima.
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