Injection of Extreme Points in Evolutionary Multiobjective Optimization Algorithms

This paper investigates a curious case of informed initialization technique to solve difficult multi-objective optimization MOP problems. The initial population was injected with non-exact i.e. approximated nadir objective vectors, which are the boundary solutions of a Pareto optimal front PF. The algorithm then successively improves those boundary solutions and utilizes them to generate non-dominated solutions targeted to the vicinity of the PF along the way. The proposed technique was ported to a standard Evolutionary Multi-objective Optimization EMO algorithm and tested on a wide variety of benchmark MOP problems. The experimental results suggest that the proposed approach is very helpful in achieving extremely fast convergence, especially if an experimenter's goal is to find a set of well distributed trade-off solutions within a fix-budgeted solution evaluations SEs. The proposed approach also ensures a more focused exploration of the underlying search space.

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