Multi-objective Differential Evolution Algorithm based on Adaptive Mutation and Partition Selection

A multi-objective differential evolution algorithm based on adaptive mutation strategies and partition selected search is proposed based on classical differential evolution(DE) to further improve the convergence and diversity of multi-objective optimization problems. This algorithm improves mutation operation in DE, makes search oriented and ensures the convergence of algorithm by adaptively selecting mutation strategies based on the non-inferiority of the individuals of the population in evolution. In addition, a partition-based elitist preserving mechanism is applied to select the best individuals for the next generation, thus improving the selection operation in DE and maintaining the diversity of Pareto optimal set. The experiment on 5 ZDT test functions and 3 DTLZ test functions and comparison with and analysis of other classical algorithms such as NSGA-II and SPEA2 show that this algorithm converges the populations towards non-inferior frontier rapidly on the premise of maintaining the diversity of the populations. From the measure and graphs, it can be seen that this algorithm is feasible and effective in solving the multi-objective optimization problems.

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

[2]  Ponnuthurai N. Suganthan,et al.  Multi-objective optimization using self-adaptive differential evolution algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Wenyin Gong,et al.  Hybrid Differential Evolution with Convex Mutation , 2011, J. Softw..

[4]  Carlos A. Coello Coello,et al.  MODE-LD+SS: A novel Differential Evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization , 2010, IEEE Congress on Evolutionary Computation.

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

[6]  K. Price.  Differential evolution vs. the functions of the 2/sup nd/ ICEO , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

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

[8]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[9]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  K..  Multiobjective Optimization Using a Pareto Differential Evolution Approach , 2022 .

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

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Lei Peng,et al.  A Novel Differential Evolution with Uniform Design for Continuous Global Optimization , 2012, J. Comput..

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

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

[16]  Wei-Ping Lee,et al.  A Self-Adaptive Differential Evolution Algorithm with Dimension Perturb Strategy , 2011, J. Comput..