Genetic Programming and Evolvable Machines

, Volume 6, Issue 2, pp 163–190

Solving Multiobjective Optimization Problems Using an Artificial Immune System

Article

DOI: 10.1007/s10710-005-6164-x

Cite this article as:
Coello, C.A.C. & Cortés, N.C. Genet Program Evolvable Mach (2005) 6: 163. doi:10.1007/s10710-005-6164-x

Abstract

In this paper, we propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the “not so good” antibodies (which are represented by binary strings that encode the decision variables of the problem to be solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimization. For our comparative study, three metrics are adopted and graphical comparisons with respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimization problems.

Keywords

artificial immune systemmultiobjective optimizationclonal selection

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Carlos A. Coello Coello
    • 1
  • Nareli Cruz Cortés
    • 1
  1. 1.Av. Instituto Politécnico Nacional No. 2508CINVESTAV-IPN, Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de ComputaciónCol. San Pedro Zacatenco, MéxicoMexico