Dynamic Multiobjective Clonal Selection Algorithm for Engineering Design

• Published in: IEEE transactions on magnetics Vol. 46; no. 8; pp. 3033 - 3036
• Main Authors: Batista, Lucas S., Oliveira, Diogo B., Guimaraes, Frederico G., Silva, Elson J., Ramirez, Jaime A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.08.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; clonal selection algorithm; Cloning; Cross-disciplinary physics: materials science; rheology; Design engineering; Dynamics; Engineering design; Evolution; Exact sciences and technology; Genetic algorithms; Genetic mutations; Heuristic algorithms; Magnetism; Materials science; Mathematical analysis; Microwaves; Multiobjective optimization; Mutations; Other topics in materials science; Performance analysis; Physics; Probability distribution; self-adaptation; Sorting; Testing; Design; clonal selection algorithm; self-adaptation; Multiobjective optimization; Magnetic properties
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2010.2044144

We propose a Multiobjective Clonal Selection Algorithm (MCSA) with dynamic variation of its main parameters for the solution of engineering design problems. The MCSA performs a cloning process using different probability distributions, in which the mutation strengths are guided based on a logarithmic rule and on information implicitly created by a simple differential evolution technique. This feature results in a self-adapting search in the algorithm. The efficiency of the MCSA is studied comparing its performance with the Nondominated Sorting Genetic Algorithm II (NSGA-II) in analytical test problems and also in the design of a microwave heating device. The MCSA has outperformed the NSGA-II in all problems investigated.