Improved Imperialist Competitive Algorithm for Constrained Optimization

This paper introduces an improved evolutionary algorithm based on the Imperialist Competitive Algorithm. The original approach in the Imperialist Competitive Algorithm has difficulty in implement practically with the increase of the dimension of the search spaces, as the ambiguous definition of the “random angle” in the process of optimization. Compare to the original algorithm, the proposed approach based on the concept of small probability perturbation has more simplicity to be implemented, especially in solving high-dimensional optimization problems. Furthermore, the present algorithm has been extended to constrained optimization problem, using a classical penalty technique to handle constraints. Several numerical optimization examples are tested by applying the proposed algorithm, and the results show its applicability and flexibility in dealing with different types of optimization problems.

[1]  Caro Lucas,et al.  Vehicle Fuzzy Controller Design Using Imperialist Competitive Algorithm , 2008 .

[2]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[3]  H. H. Balci,et al.  Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method , 2004 .

[4]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..

[5]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[6]  Z. Bingul,et al.  A new PID tuning technique using ant algorithm , 2004, Proceedings of the 2004 American Control Conference.

[7]  Kalyanmoy Deb,et al.  Optimal design of a welded beam via genetic algorithms , 1991 .

[8]  C. Darwin.  On the Origin of Species by Means of Natural Selection: Or, The Preservation of Favoured Races in the Struggle for Life , 2019 .

[9]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[10]  Christophe Andrieu,et al.  Simulated annealing for maximum a Posteriori parameter estimation of hidden Markov models , 2000, IEEE Trans. Inf. Theory.

[11]  David Mautner Himmelblau,et al.  Applied Nonlinear Programming , 1972 .

[12]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[13]  K. M. Ragsdell,et al.  Optimal Design of a Class of Welded Structures Using Geometric Programming , 1976 .

[14]  C. Darwin.  Charles Darwin The Origin of Species by means of Natural Selection or The Preservation of Favoured Races in the Struggle for Life , 2004 .

[15]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[16]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

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

[18]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[19]  A. E. Eiben,et al.  Evolutionary Programming VII , 1998, Lecture Notes in Computer Science.

[20]  James N. Siddall,et al.  Analytical decision-making in engineering design , 1972 .

[21]  Robert G. Reynolds,et al.  Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming , 1995 .

[22]  Roy L. Johnston,et al.  Applications of Evolutionary Computation in Chemistry , 2004 .