Assessing hypermutation operators of a clonal selection algorithm for the unequal area facility layout problem

A mutation operator is critical for the performance of a clonal selection algorithm (CSA) since it diversifies the search directions and avoids early convergence to local optima. This article introduces a CSA approach for the unequal area facility layout problem (UAFLP) with flexible bay structure. A new encoding, the use of mutation types with different combinations, and different static and dynamic mutation application strategies are also proposed. In addition, a guideline in parameter optimization of the CSA is provided. An experimental study is performed on five cases of the UAFLP. It is concluded that the hypermutation types studied in this article, especially the inverse mutation followed by pairwise mutation, can be used to obtain good results within short computation times.

[1]  Elwood S. Buffa,et al.  A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities , 1963 .

[2]  Artak Hakobyan,et al.  Heuristics for the dynamic facility layout problem with unequal-area departments , 2010, Eur. J. Oper. Res..

[3]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[4]  Sadan Kulturel-Konak,et al.  A review of clonal selection algorithm and its applications , 2011, Artificial Intelligence Review.

[5]  Russell D. Meller,et al.  The facility layout problem: Recent and emerging trends and perspectives , 1996 .

[6]  Dipankar Dasgupta,et al.  Immunological Computation: Theory and Applications , 2008 .

[7]  Daniel Scholz,et al.  STaTS: A Slicing Tree and Tabu Search based heuristic for the unequal area facility layout problem , 2009, Eur. J. Oper. Res..

[8]  Komarudin,et al.  Applying Ant System for solving Unequal Area Facility Layout Problems , 2010, Eur. J. Oper. Res..

[9]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[10]  P. Helman,et al.  A formal framework for positive and negative detection schemes , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Mario Enea,et al.  The facility layout problem approached using a fuzzy model and a genetic search , 2005, J. Intell. Manuf..

[12]  D. Camp,et al.  A nonlinear optimization approach for solving facility layout problems , 1992 .

[13]  Brian L. Joiner,et al.  MINITAB Handbook: Update for Release 16 , 1985 .

[14]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[15]  Alice E. Smith,et al.  Unequal-area facility layout by genetic search , 1995 .

[16]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[17]  Komarudin,et al.  Solving facility layout problems using Flexible Bay Structure representation and Ant System algorithm , 2010, Expert Syst. Appl..

[18]  Ivanoe De Falco,et al.  Mutation-based genetic algorithm: performance evaluation , 2002, Appl. Soft Comput..

[19]  Henri Pierreval,et al.  Facility layout problems: A survey , 2007, Annu. Rev. Control..

[20]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[21]  Georges R. Harik,et al.  Foundations of Genetic Algorithms , 1997 .

[22]  Abdullah Konak,et al.  Unequal area flexible bay facility layout using ant colony optimisation , 2011 .

[23]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[24]  Stephanie Forrest,et al.  Infect Recognize Destroy , 1996 .

[25]  Simon M. Garrett.  Parameter-free, adaptive clonal selection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[26]  Kalyanmoy Deb,et al.  Accounting for Noise in the Sizing of Populations , 1992, FOGA.

[27]  L. N. de Castro.  Immune, swarm, and evolutionary algorithms. Part II: philosophical comparisons , 2002 .

[28]  David W. Coit,et al.  Exploiting Tabu Search Memory in Constrained Problems , 2004, INFORMS J. Comput..

[29]  Teofilo F. Gonzalez,et al.  P-Complete Approximation Problems , 1976, J. ACM.

[30]  S. Forrest,et al.  Antibody repertoires and pathogen recognition: the role of germline diversity and somatic hypermutation , 1999 .

[31]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[32]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[33]  Claudia Eckert,et al.  Is negative selection appropriate for anomaly detection? , 2005, GECCO '05.

[34]  Wen-Chyuan Chiang,et al.  An improved tabu search heuristic for solving facility layout design problems , 1996 .

[35]  Engelbert Westkämper,et al.  A coevolutionary algorithm for a facility layout problem , 2003 .

[36]  David M. Tate,et al.  Unequal Area Facility Layout Using Genetic Search , 1994 .

[37]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[38]  R. D. Meller.  The multi-bay manufacturing facility layout problem , 1997 .

[39]  Yavuz A. Bozer,et al.  A new simulated annealing algorithm for the facility layout problem , 1996 .

[40]  Rrk Sharma,et al.  A review of different approaches to the facility layout problems , 2006 .

[41]  Alice E. Smith,et al.  Bi-objective facility expansion and relayout considering monuments , 2007 .

[42]  Dale Farris,et al.  Design of Experiments With MiNITAB , 2005 .

[43]  Abdullah Konak,et al.  A new mixed integer programming formulation for facility layout design using flexible bays , 2006, Oper. Res. Lett..

[44]  Berna Haktanirlar Ulutaş,et al.  Parameter Setting for Clonal Selection Algorithm in Facility Layout Problems , 2007, ICCSA.

[45]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[46]  S. Kumanan,et al.  Artificial immune system-based algorithm for the unidirectional loop layout problem in a flexible manufacturing system , 2009 .

[47]  K.-Y. Gau,et al.  An iterative facility layout algorithm , 1999 .

[48]  Jonathan Timmis,et al.  Theoretical advances in artificial immune systems , 2008, Theor. Comput. Sci..

[49]  Alper Döyen,et al.  A new approach to solve hybrid flow shop scheduling problems by artificial immune system , 2004, Future Gener. Comput. Syst..

[50]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[51]  Zbigniew Michalewicz,et al.  Parameter control in evolutionary algorithms , 1999, IEEE Trans. Evol. Comput..

[52]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[53]  Jason Brownlee,et al.  Clonal selection algorithms , 2007 .

[54]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.