Application of Genetic Algorithm in Multi-objective Optimization

of management science, operations research, and industrial and system engineering, the application
of GAs is increasing day by day because of the advantages of GAs over the conventional methods
[8, 20, 22]. They have also been successfully applied in different real-world scenarios, for example in
aeronautics, electrical engineering, scheduling and signal processing, etc. The concept of using GAs
was first introduced by Holland [7]. Then it was developed theoretically [7] and applied in various
fields [8]. Simpson et al. [23] used simple GAs and Dandy et al. [24] experimented with the fitness
function, mutation operator, and gray codes. Abdel-Gawad [25] showed and explained the effect of
different selection, crossover and mutation schemes of the GAs on the network optimization.
2.3.1. GA Operators:
2.3.1.1. Initialization:
In GAs, decision variables or parameters are encoded and a set of initial solutions, called the
population, is generated. GAs operate on the population simultaneously. A random generator is
used to generate the required number of initial individuals or population within the desired range.
Bramlette [26] suggested an approach of generating individuals where for each individual a number
of individuals are generated, and the best-performed one is selected for the initial population.
Another approach, which is only applicable to well-known problems, is to initialize the population
with some individuals from the vicinity of global optimal results [27, 28]. The binary string
representation is the most popular representation where each variable is encoded in the binary string
and concatenated to form a complete chromosome. In traditional binary representation, one
problem is that the hamming distances between adjacent values are constant which affect the search
space by deceiving it while searching global optima [29]. Gray coding is used to improve the
standard system.
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