A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

AbstractMultiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosine crowding measurement. The first component is used to organize the population. Compared with a 2D grid, the 3D grid offers a vertical expansion of cells. The second one simultaneously considers the crowding distances and location distributions for measuring the crowding degree values for the solutions. The simulation results show that C-MCGA outperforms two typical MOcGAs and two state-of-the-art algorithms, NSGA-II and SPEA2, on a given set of test instances. Furthermore, the proposed measurement metric is compared with that in NSGA-II, which is demonstrated to yield a more diverse population on most of the test instances.

[1]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  Tughrul Arslan,et al.  Fault tolerance through automatic cell isolation using three-dimensional cellular genetic algorithms , 2010, IEEE Congress on Evolutionary Computation.

[3]  Enrique Alba,et al.  Cellular genetic algorithms , 2014, GECCO.

[4]  Ming Li,et al.  The Cellular Genetic Algorithms with Disaster: The Size of Disaster Effects , 2009, 2009 International Conference on Information Engineering and Computer Science.

[5]  Daniel S. Yeung,et al.  A genetic algorithm for solving the inverse problem of support vector machines , 2005, Neurocomputing.

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

[7]  Enrique Alba,et al.  Design Issues in a Multiobjective Cellular Genetic Algorithm , 2007, EMO.

[8]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

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

[10]  David R. Hardoon,et al.  Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms , 2011, Int. J. Mach. Learn. Cybern..

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

[12]  Enrique Alba,et al.  Decentralized Cellular Evolutionary Algorithms , 2005, Handbook of Bioinspired Algorithms and Applications.

[13]  Enrique Alba,et al.  MOCell: A cellular genetic algorithm for multiobjective optimization , 2009, Int. J. Intell. Syst..

[14]  Günter Rudolph,et al.  A cellular genetic algorithm with self-adjusting acceptance threshold , 1995 .

[15]  Chuen-Jyh Chen.  Structural vibration suppression by using neural classifier with genetic algorithm , 2012, Int. J. Mach. Learn. Cybern..

[16]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[17]  Jacek M. Zurada,et al.  Swarm and Evolutionary Computation , 2012, Lecture Notes in Computer Science.

[18]  Tughrul Arslan,et al.  Dynamic Fault-Tolerant three-dimensional cellular genetic algorithms , 2013, J. Parallel Distributed Comput..

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

[20]  Yi Zhang,et al.  Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm , 2012 .

[21]  L. Darrell Whitley,et al.  Cellular Genetic Algorithms , 1993, ICGA.

[22]  Enrique Alba,et al.  Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm , 2008, PPSN.

[23]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[24]  Enrique Alba,et al.  AbYSS: Adapting Scatter Search to Multiobjective Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[25]  Tughrul Arslan,et al.  Towards 3D Architectures: A Comparative Study on Cellular GAs Dimensionality , 2009, 2009 NASA/ESA Conference on Adaptive Hardware and Systems.

[26]  Yaonan Wang,et al.  Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure , 2010, Soft Comput..

[27]  Lakhmi C. Jain,et al.  Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[28]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[29]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[30]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[31]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[32]  Enrique Alba,et al.  Improving flexibility and efficiency by adding parallelism to genetic algorithms , 2002, Stat. Comput..

[33]  Aimin Zhou,et al.  MCGA: A Multiobjective Cellular Genetic Algorithm Based on a 3D Grid , 2013, IDEAL.

[34]  Max E. Valentinuzzi.  Handbook of bioinspired algorithms and applications , 2006, BioMedical Engineering OnLine.

[35]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[37]  Hisao Ishibuchi,et al.  Effects of Using Two Neighborhood Structures in Cellular Genetic Algorithms for Function Optimization , 2006, PPSN.

[38]  Tughrul Arslan,et al.  Fault tolerant three-dimensional cellular genetic algorithms with adaptive migration schemes , 2011, 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).

[39]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[40]  Tughrul Arslan,et al.  Balancing exploration and exploitation in an adaptive three-dimensional cellular genetic algorithm via a probabilistic selection operator , 2010, 2010 NASA/ESA Conference on Adaptive Hardware and Systems.

[41]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[42]  Enrique Alba,et al.  A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[43]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[44]  Weiming Shen,et al.  Effective genetic algorithm for resource-constrained project scheduling with limited preemptions , 2011, Int. J. Mach. Learn. Cybern..