Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions

The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms’ performance the majority of the existing implementations select a simplistic approach by assuming identical behavior for all candidate solutions of a population. However, from the biological analogue we know that individuals do not react similarly to the same stimulus. This is called character and it is lacking from existing implementations. In this paper, we emulate the corresponding human social analogue by generating individuals that exhibit different behavior when are subject to the same stimulus. The implementation of different behaviors is facilitated through a novel mutation operator. The experimental results favor the proposed approach when compared with other state-of-the-art algorithms for a number of test instances.