A new many-objective evolutionary algorithm based on self-adaptive differential evolution

To improve the performance of the existing multi-objective evolutionary algorithms (MOEAs), we propose a new self-adaptive differential evolution algorithm for solving many-objective optimization problems (MOPs). To address the challenges in many-objective optimization, new selection strategy and density estimation method are designed to improve the performance of the elite MOEA model used by several exiting MOEAs. In addition, new mutation strategy and parameter adaptive method of DE are proposed to enhance the convergence ability of the evolution strategy utilized in MOEAs. Experimental results on ZDT and DTLZ test problems show that, the proposed algorithm, named SDEMO, is able to find much better spread of solutions with better approximating the true Pareto-optimal front compared to six state-of-the-art MOEAs.

[1]  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.

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

[3]  Arthur C. Sanderson,et al.  Self-adaptive multi-objective differential evolution with direction information provided by archived inferior solutions , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[4]  Rainer Laur,et al.  Variants of Differential Evolution for Multi-Objective Optimization , 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

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

[6]  Huiru Zheng,et al.  Integration of Genomic Data for Inferring Protein Complexes from Global Protein–Protein Interaction Networks , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[8]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

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

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

[11]  Xiaodong Li,et al.  Solving Rotated Multi-objective Optimization Problems Using Differential Evolution , 2004, Australian Conference on Artificial Intelligence.

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

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

[14]  Lishan Kang,et al.  A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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