TY - JOUR
T1 - Solving multiobjective optimization problems using an artificial immune system
AU - Coello, Carlos A.Coello
AU - Cortés, Nareli Cruz
N1 - Funding Information:
We thank the comments of the anonymous reviewers that greatly helped us to improve the contents of this paper. The first author gratefully acknowledges support from CONA-CyT through project 34201-A. The second author acknowledges support from CONACyT through a scholarship to pursue graduate studies at the Computer Science Section of the Electrical Engineering Department at CINVESTAV-IPN.
PY - 2005/6
Y1 - 2005/6
N2 - 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.
AB - 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.
KW - Artificial immune system
KW - Clonal selection
KW - Multiobjective optimization
UR - http://www.scopus.com/inward/record.url?scp=17444430405&partnerID=8YFLogxK
U2 - 10.1007/s10710-005-6164-x
DO - 10.1007/s10710-005-6164-x
M3 - Artículo
SN - 1389-2576
VL - 6
SP - 163
EP - 190
JO - Genetic Programming and Evolvable Machines
JF - Genetic Programming and Evolvable Machines
IS - 2
ER -