Combining Dynamic Constrained Many-Objective Optimization with DE to Solve Constrained Optimization Problems

This paper proposes a dynamic constrained many-objective optimization method for solving constrained optimization problems. We first convert a constrained optimization problem (COP) into an equivalent dynamic constrained many-objective optimization problem (DCMOP), then present many-objective optimization evolutionary algorithm with dynamic constraint handling mechanism, called MaDC, to solve the DCMOP, thus the COP is addressed. MaDC uses DE as the search engine, and reference-point-based nondominated sorting approach to select individuals to construct next population. The effectiveness of MaDC has been verified by comparing with peer algorithms.

[1]  C. Coello,et al.  CONSTRAINT-HANDLING USING AN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION TECHNIQUE , 2000 .

[2]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[3]  Tapabrata Ray,et al.  Differential Evolution With Dynamic Parameters Selection for Optimization Problems , 2014, IEEE Transactions on Evolutionary Computation.

[4]  Gary G. Yen,et al.  A generic framework for constrained optimization using genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[5]  Aimin Zhou,et al.  Dynamic constrained multi-objective model for solving constrained optimization problem , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[6]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[7]  P. N. Suganthan,et al.  Ensemble of Constraint Handling Techniques , 2010, IEEE Transactions on Evolutionary Computation.

[8]  Li-Chen Fu,et al.  A hybrid constraint handling mechanism with differential evolution for constrained multiobjective optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[9]  Oliver Kramer,et al.  A Review of Constraint-Handling Techniques for Evolution Strategies , 2010, Appl. Comput. Intell. Soft Comput..

[10]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[11]  Xi Li,et al.  Constrained optimization problem solved by dynamic constrained NSGA-III multiobjective optimizational techniques , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[12]  Kalyanmoy Deb,et al.  A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach , 2010, IEEE Congress on Evolutionary Computation.

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

[14]  Ruhul A. Sarker,et al.  Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..

[15]  Yuren Zhou,et al.  Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).