Self-adaptive root growth model for constrained multi-objective optimization

This paper presents a general optimization model gleaned ideas from plant root growth behaviors in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for constrained multi-objective optimization. A novel method called “multi-objective root growth algorithm” (MORGA) for constrained multi-objective optimization is proposed based on the root growth model. A self-adaptive strategy is adopted to tie this model closer to plant root growth behaviors in nature, as well as improve the robustness of MORGA. Simulation experiments of MORGA on a set of benchmark test functions are compared with other nature inspired techniques for multi-objective optimization which includes nondominated sorting genetic algorithmII (NSGAII) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MORGA approach is a powerful search and optimization technique for constrained multi-objective optimization.

[1]  Masatoshi Sakawa,et al.  An Interactive Fuzzy Satisficing Method for Multiobjective Linear Programming Problems with Fuzzy Parameters , 1987 .

[2]  Carlos A. Coello Coello,et al.  Constraint-handling in nature-inspired numerical optimization: Past, present and future , 2011, Swarm Evol. Comput..

[3]  Qingfu Zhang,et al.  The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

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

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

[7]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[9]  Gary G. Yen,et al.  Constraint handling in multi-objective evolutionary optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

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

[11]  Ponnuthurai N. Suganthan,et al.  Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection , 2010, Inf. Sci..

[12]  R. Landauer,et al.  Thinking in Complexity: The Complex Dynamics of Matter, Mind, and Mankind , 1995 .

[13]  Ponnuthurai Nagaratnam Suganthan,et al.  Two-lbests based multi-objective particle swarm optimizer , 2011 .

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

[15]  Tetsuyuki Takahama,et al.  Constrained Optimization by the alpha Constrained Particle Swarm Optimizer , 2005, J. Adv. Comput. Intell. Intell. Informatics.

[16]  Hao Zhang,et al.  A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production , 2012 .

[17]  Yunlong Zhu,et al.  Root Growth Model for Simulation of Plant Root System and Numerical Function Optimization , 2012, ICIC.

[18]  P. Suganthan,et al.  Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods , 2011 .

[19]  Masatoshi Sakawa,et al.  An Interactive Fuzzy Satisficing Method for Multiobjective Linear-Programming Problems and Its Application , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

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

[21]  Gary B. Lamont,et al.  Evolutionary algorithms for solving multi-objective problems, Second Edition , 2007, Genetic and evolutionary computation series.