Comparative Study on Multi-Objective Genetic Algorithms for Seismic Response Controls of Structures - Design Optimization of Active and Passive Structural Control Systems - page 341

Figure 4. Flow chart of NS2-IRR GA (Cha 2008)

SP2-IRR GA

P t +1 the crowding distances are assigned to the
best remedy rank F i using Equation (5). By add-
ing F i to P t +1 and sorting in descending order by
using Equation (6), the first N number of indi-
viduals is selected. For the fourth step, Q t +1 is
generated by crossover and mutation operator.
This iteration will continue until satisfying the
GA criteria or reaching the predefined maximum
generation. Although the performance of the NS2-
IRR GA is good, it is often difficult to guarantee
diverse optimal solution sets. However, a solution
can be found in the modified strength Pareto
evolutionary algorithm.

Strength Pareto Evolutionary
Algorithm (SPEA)

Zitzler and Thiele (1999) proposed the strength
Pareto evolutionary algorithm (SPEA) with com-
bination of several features of multi-objective
evolutionary algorithms in a unique manner.
The SPEA has some similarities in its process to
other evolutionary algorithms (Zitzler and Thiele
1999) in that it