Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems

One of the challenges that appear in solving constrained optimization problems is to quickly locate the search areas of interest. Although the initial solutions of any optimization algorithm have a significant effect on its performance, none of the existing initialization methods can provide direct information about the objective function and constraints of the problem to be solved. In this paper, a technique for generating initial solutions is proposed, which provides useful information about the behavior of both the objective function and the constraints. Based on such information, an automatic mechanism for selecting individuals, from the search areas of interest, is introduced. The proposed method is adopted with different evolutionary algorithms and tested on the CEC2006 and the CEC2010 test problems. The results obtained show the benefits of the proposed method in enhancing the performance, and reducing the average computational time, of several algorithms with respect to their versions adopting other initialization techniques

[1]  Jing Wang,et al.  A New Population Initialization Method Based on Space Transformation Search , 2009, 2009 Fifth International Conference on Natural Computation.

[2]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[3]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[4]  K. Miettinen,et al.  Quasi-random initial population for genetic algorithms , 2004 .

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

[6]  Rolf Wanka,et al.  Theoretical Analysis of Initial Particle Swarm Behavior , 2008, PPSN.

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

[8]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.

[9]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[10]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[11]  A. Kai Qin,et al.  A review of population initialization techniques for evolutionary algorithms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[12]  Carlos A. Coello Coello,et al.  Sequence-Based Deterministic Initialization for Evolutionary Algorithms , 2017, IEEE Transactions on Cybernetics.

[13]  Ruhul A. Sarker,et al.  Enhanced multi-operator differential evolution for constrained optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[14]  Tapabrata Ray,et al.  Adaptation of operators and continuous control parameters in differential evolution for constrained optimization , 2018, Soft Comput..

[15]  Na Wang,et al.  Influence of Dimensionality and Population Size on Opposition-based Differential Evolution Using the Current Optimum , 2013 .

[16]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Ahmet Bedri Özer,et al.  CIDE: Chaotically Initialized Differential Evolution , 2010, Expert Syst. Appl..

[18]  Wenyin Gong,et al.  Enhancing the performance of differential evolution using orthogonal design method , 2008, Appl. Math. Comput..

[19]  Shahryar Rahnamayan,et al.  Center-based sampling for population-based algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[20]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[21]  K Ang,et al.  A NOTE ON UNIFORM DISTRIBUTION AND EXPERIMENTAL DESIGN , 1981 .

[22]  H. Schuster Deterministic chaos: An introduction , 1984 .

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