ENGINEERING OPTIMIZATION WITH HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION

This article presents a heuristic particle swarm ant colony optimization algorithm to solve engineering optimization problems. Although PSO has simple principle and ease to be implemented and can eventually locate the desired solution, however, its practical use in solving engineering optimization problems is severely limited by the high computational cost of the slow convergence rate. Here, ant colony and harmony search principles are employed to speed up local search and improve precision of the solutions. A modified feasible-based mechanism is described which handles the problem-specific constraints. Benchmark optimization problems are used to illustrate the reliability of the proposed algorithm.

[1]  Shu-Kai S. Fan,et al.  A hybrid simplex search and particle swarm optimization for unconstrained optimization , 2007, Eur. J. Oper. Res..

[2]  Siamak Talatahari,et al.  A DISCRETE PARTICLE SWARM ANT COLONY OPTIMIZATION FOR DESIGN OF STEEL FRAMES , 2008 .

[3]  Stephen J. J. Smith.  The simplex method and evolutionary algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[5]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[6]  Siamak Talatahari,et al.  Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures , 2009 .

[7]  Ling Wang,et al.  An effective hybrid PSOSA strategy for optimization and its application to parameter estimation , 2006, Appl. Math. Comput..

[8]  Peng-Yeng Yin,et al.  Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique , 2008 .

[9]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[10]  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 .

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

[12]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[13]  M. Mahdavi,et al.  ARTICLE IN PRESS Available online at www.sciencedirect.com , 2007 .

[14]  Carlos A. Coello Coello,et al.  An empirical study about the usefulness of evolution strategies to solve constrained optimization problems , 2008, Int. J. Gen. Syst..

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

[16]  Wei Kong,et al.  Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data , 2008, Comput. Biol. Chem..

[17]  Siamak Talatahari,et al.  A particle swarm ant colony optimization for truss structures with discrete variables , 2009 .

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

[19]  Peng-YengYin,et al.  Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique , 2008 .

[20]  Siamak Talatahari,et al.  Ant Colony Optimization for Design of Space Trusses , 2008 .

[21]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[22]  Siamak Talatahari,et al.  An improved ant colony optimization for constrained engineering design problems , 2010 .

[23]  Ali Kaveh,et al.  A HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION FOR DESIGN OF TRUSS STRUCTURES , 2008 .

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

[25]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[26]  D. Y. Sha,et al.  A hybrid particle swarm optimization for job shop scheduling problem , 2006, Comput. Ind. Eng..

[27]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[28]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

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

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

[31]  A. Kaveh,et al.  Size optimization of space trusses using Big Bang-Big Crunch algorithm , 2009 .

[32]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

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

[34]  Patrick Siarry,et al.  Particle swarm and ant colony algorithms hybridized for improved continuous optimization , 2007, Appl. Math. Comput..

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

[36]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[37]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .