A Real-Integer-Discrete-Coded Differential Evolution Algorithm: A Preliminary Study

The successful application of differential evolution (DE) algorithms to various real-valued problems encourages to develop some integer-coded versions of DE for working directly with integer and discrete variables of a problem. However, in most of those works, actually a real-valued solution is just converted into a desired integer-valued solution by applying some decoding mechanisms. Only a limited number of works are found, in which attempts are made for developing an actual integer-coded DE. In this article, a novel version of DE is proposed which can work directly with real, integer and discrete variables of a problem without any conversion. Applying to two non-linear real-integer-discrete-valued engineering design problems, the proposed DE is found successful in obtaining the known best solutions of the problems.

[1]  S. Wu,et al.  GENETIC ALGORITHMS FOR NONLINEAR MIXED DISCRETE-INTEGER OPTIMIZATION PROBLEMS VIA META-GENETIC PARAMETER OPTIMIZATION , 1995 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Kalyanmoy Deb,et al.  Multi-objective Evolutionary Algorithms for Resource Allocation Problems , 2007, EMO.

[5]  A. Sima Etaner-Uyar,et al.  Experimental analysis of binary differential evolution in dynamic environments , 2007, GECCO '07.

[6]  Chun Zhang,et al.  ON MIXED-DISCRETE NONLINEAR OPTIMIZATION PROBLEMS: A COMPARATIVE STUDY , 1995 .

[7]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[8]  Andries Petrus Engelbrecht,et al.  Binary differential evolution strategies , 2007, 2007 IEEE Congress on Evolutionary Computation.

[9]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[10]  Andries Petrus Engelbrecht,et al.  Binary Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  Tao Gong,et al.  Differential Evolution for Binary Encoding , 2007 .

[12]  José Rui Figueira,et al.  Graph partitioning by multi-objective real-valued metaheuristics: A comparative study , 2011, Appl. Soft Comput..

[13]  H. Loh,et al.  A Sequential Linearization Approach for Solving Mixed-Discrete Nonlinear Design Optimization Problems , 1991 .

[14]  R. Balamurugan,et al.  Hybrid integer coded differential evolution – dynamic programming approach for economic load dispatch with multiple fuel options , 2008 .

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

[16]  C. Guo,et al.  Swarm intelligence for mixed-variable design optimization , 2004, Journal of Zhejiang University. Science.

[17]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[18]  Jahau Lewis Chen,et al.  Optimal design of machine elements using genetic algorithms , 1993 .

[19]  Chun Zhang,et al.  Mixed-discrete nonlinear optimization with simulated annealing , 1993 .

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

[21]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method , 2004 .

[22]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[23]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 2 : a practical example , 1999 .