Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection

Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

[1]  Chao Liu,et al.  Hybrid immune algorithm with Lamarckian local search for multi-objective optimization , 2010, Memetic Comput..

[2]  Jeffrey Horn,et al.  Multiobjective Optimization Using the Niched Pareto Genetic Algorithm , 1993 .

[3]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[4]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

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

[6]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Zhou Ji,et al.  Revisiting Negative Selection Algorithms , 2007, Evolutionary Computation.

[8]  Maoguo Gong,et al.  Clonal Selection with Immune Dominance and Anergy Based Multiobjective Optimization , 2005, EMO.

[9]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[10]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[11]  J. Periaux,et al.  Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems , 2001 .

[12]  T. Fukuda,et al.  Immune Networks Using Genetic Algorithm for Adaptive Production Scheduling , 1993 .

[13]  G. Rudolph.  On a multi-objective evolutionary algorithm and its convergence to the Pareto set , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  Kalyanmoy Deb,et al.  Self-Adaptive Genetic Algorithms with Simulated Binary Crossover , 2001, Evolutionary Computation.

[15]  Stefan Roth,et al.  Covariance Matrix Adaptation for Multi-objective Optimization , 2007, Evolutionary Computation.

[16]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[17]  L. Jiao,et al.  Immune secondary response and clonal selection inspired optimizers , 2009 .

[18]  Yang Dong,et al.  Research on Evolutionary Multi-Objective Optimization Algorithms , 2009 .

[19]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[20]  A. George,et al.  Receptor editing during affinity maturation. , 1999, Immunology today.

[21]  D.A. Van Veldhuizen,et al.  On measuring multiobjective evolutionary algorithm performance , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[22]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[23]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[24]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[25]  Maoguo Gong,et al.  Intelligent multi-user detection using an artificial immune system , 2009, Science in China Series F: Information Sciences.

[26]  Vincenzo Cutello,et al.  A Class of Pareto Archived Evolution Strategy Algorithms Using Immune Inspired Operators for Ab-Initio Protein Structure Prediction , 2005, EvoWorkshops.

[27]  Lothar Thiele,et al.  A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .

[28]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[29]  P. Kourilsky,et al.  T-cell repertoire diversity and clonal expansions in normal and clinical samples. , 1995, Immunology today.

[30]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[31]  C. Coello,et al.  Multiobjective optimization using a micro-genetic algorithm , 2001 .

[32]  Miroslav Kubat,et al.  Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers , 2010, IEEE Transactions on Knowledge and Data Engineering.

[33]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[34]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[35]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[36]  Maoguo Gong,et al.  Corrections on the Box Plots of the Coverage Metric in Multiobjective Immune Algorithm with Nondominated Neighbor-based Selection , 2009, Evolutionary Computation.

[37]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[38]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[39]  Carlos A. Coello Coello,et al.  Solving Multiobjective Optimization Problems Using an Artificial Immune System , 2005, Genetic Programming and Evolvable Machines.

[40]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[41]  S. Baranzini,et al.  B cell repertoire diversity and clonal expansion in multiple sclerosis brain lesions. , 1999, Journal of immunology.

[42]  Carlos A. Coello Coello,et al.  Recent Trends in Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[43]  Carlos A. Coello Coello,et al.  Evolutionary Multiobjective Optimization: Current and Future Challenges , 2003 .

[44]  Uwe Aickelin,et al.  The Danger Theory and Its Application to Artificial Immune Systems , 2008, ArXiv.

[45]  J. Parkin,et al.  An overview of the immune system , 2001, The Lancet.

[46]  Vincenzo Cutello,et al.  Artificial Immune Systems: Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13-16, 2004, Proceedings (Lecture Notes in Computer Science) , 2004 .

[47]  Kalyanmoy Deb,et al.  Running performance metrics for evolutionary multi-objective optimizations , 2002 .

[48]  Vincenzo Cutello,et al.  Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators , 2004, ICARIS.

[49]  D. Dasgupta,et al.  A formal model of an artificial immune system. , 2000, Bio Systems.

[50]  Tong Heng Lee,et al.  Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

[51]  Fabio Freschi,et al.  Multiobjective Optimization by a Modified Artificial Immune System Algorithm , 2005, ICARIS.

[52]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[53]  J. Tukey,et al.  Variations of Box Plots , 1978 .

[54]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[55]  Maoguo Gong,et al.  ADAPTIVE MULTI‐OBJECTIVE OPTIMIZATION BASED ON NONDOMINATED SOLUTIONS , 2009, Comput. Intell..

[56]  Fang Liu,et al.  Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization , 2010, Knowledge and Information Systems.

[57]  C. A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Computational Intelligence Magazine.

[58]  Marc Schoenauer,et al.  A Steady Performance Stopping Criterion for Pareto-based Evolutionary Algorithms , 2004 .

[59]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[60]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

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

[62]  Xin Yao,et al.  Performance Scaling of Multi-objective Evolutionary Algorithms , 2003, EMO.

[63]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[64]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.