Memetic algorithm for dynamic bi-objective optimization problems

Dynamic multi-objective optimization (DMO) is a challenging class of problems where the objective and/or the constraint function(s) change over time. DMO has received little attention in the past and none of the existing multi-objective optimization algorithms have performed too well on the set DMO test problems. In this paper, we introduce a memetic algorithm (MA) embedded with a sequential quadratic programming (SQP) solver for faster convergence and an orthogonal epsilon-constrained formulation is used to deal with two objectives. The performance of the memetic algorithm is compared with an evolutionary algorithm (EA) embedded with a Sub-EA with and without restart mechanisms on two benchmark functions FDA1 and modified FDA2. The memetic algorithm consistently outperforms the evolutionary algorithm for both FDA1 and modified FDA2 problems.