Memetic algorithm for dynamic bi-objective optimization problems

Authors:
Amitay Isaacs;Tapabrata Ray;Warren Smith
Affiliations:
School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT, Australia
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

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 multiobjective 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.