A new constrained multiobjective optimization algorithm based on artificial immune systems

This paper proposes a new constrained multiobjective optimization algorithm based on artificial immune systems (AIS). To deal with constrained multiobjective optimization problems, the constrained AIS-based multiobjective optimization algorithm is developed by integrating a proposed constraint-handling technique with the unconstrained AIS-based multiobjective optimization algorithm named MOAIS [1]. We propose the constraint-handling technique by extending a single-objective constraint-handling technique called stochastic ranking [2] to multiobjective optimization process. Two scenarios of the multiobjective version of stochastic ranking are suggested. Thereafter, we develop the constrained MOAIS named MOAIS+SR by integrating the two scenarios with MOAIS. A comparative study is performed quantitatively to assess the performance of MOAIS+SR on a constrained test function suite called CTP test problems. In the comparative study, MOAIS+SR is compared against two other constrained multiobjective algorithms. The simulation results show that the proposed multiobjective stochastic ranking outperforms the constrained-dominance principle [3] in handling constraints. Furthermore, we show that the proposed MOAIS+SR achieves the best overall performance among the three algorithms under consideration on the CTP test problems. This study demonstrates that the proposed MOAIS+SR is highly competitive with other state-of-the-art algorithms in constrained multiobjective optimization.