Immune generalized differential evolution for dynamic multiobjective optimization problems

In this paper a multiobjective differential evolution algorithm called Generalized Differential Evolution is extended to solve dynamic multiobjective optimization problems (DMOPs). The proposed algorithm combines the ideas of the generalized differential evolution and the artificial immune system to create a hybrid algorithm which uses the advantages of both approaches. When a change is detected in the environment by a solution reevaluation mechanism, an immune response is activated. The approach is compared against other dynamic multiobjective algorithms in a recently proposed benchmark. Experimental results show that the proposed approach can track the environmental change and has a very competitive performance solving different types of DMOPs.

[1]  Carlos A. Coello Coello,et al.  Multiobjective Optimization Using Ideas from the Clonal Selection Principle , 2003, GECCO.

[2]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[3]  Jouni Lampinen,et al.  COMPARISON OF GENERALIZED DIFFERENTIAL EVOLUTION TO OTHER MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS , 2004 .

[4]  Ponnuthurai N. Suganthan,et al.  Evolutionary multiobjective optimization in dynamic environments: A set of novel benchmark functions , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[5]  Kalyanmoy Deb,et al.  Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[6]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[8]  Zhuhong Zhang,et al.  Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems , 2011, Soft Comput..

[9]  C. Coello,et al.  Years of Evolutionary Multi-Objective Optimization : What Has Been Done and What Remains To Be Done , 2006 .

[10]  Zhuhong Zhang,et al.  Immune Optimization Approach for Dynamic Constrained Multi-Objective Multimodal Optimization Problems , 2012 .

[11]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[12]  Andries Petrus Engelbrecht,et al.  Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[14]  Maoguo Gong,et al.  Clonal Selection Algorithm for Dynamic Multiobjective Optimization , 2005, CIS.

[15]  Qingfu Zhang,et al.  A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[16]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[17]  Hussein A. Abbass,et al.  A multi-objective evolutionary method for Dynamic Airspace Re-sectorization using sectors clipping and similarities , 2012, 2012 IEEE Congress on Evolutionary Computation.

[18]  Jouni Lampinen,et al.  An Extension of Generalized Differential Evolution for Multi-objective Optimization with Constraints , 2004, PPSN.

[19]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

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

[21]  Efrén Mezura-Montes,et al.  Differential evolution in constrained numerical optimization: An empirical study , 2010, Inf. Sci..

[22]  D. Rathbun,et al.  An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[23]  Jonathan Timmis,et al.  Noname manuscript No. (will be inserted by the editor) On Artificial Immune Systems and Swarm Intelligence , 2022 .

[24]  Zbigniew Michalewicz,et al.  An evolutionary multi-objective approach for dynamic mission planning , 2010, IEEE Congress on Evolutionary Computation.

[25]  Hugo de Garis,et al.  A Dynamic Multi-Objective Evolutionary Algorithm Based on an Orthogonal Design , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[26]  Lam Thu BUI,et al.  An adaptive approach for solving dynamic scheduling with time-varying number of tasks — Part I , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[27]  Jouni Lampinen,et al.  GDE3: the third evolution step of generalized differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[28]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[29]  Gregorio Toscano Pulido,et al.  Handling Dynamic Multiobjective Problems with Particle Swarm Optimization , 2010, ICAART.

[30]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.

[31]  Xiaodong Li,et al.  On performance metrics and particle swarm methods for dynamic multiobjective optimization problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[32]  Zhuhong Zhang,et al.  Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control , 2008, Appl. Soft Comput..