SOCIAL HARMONY SEARCH ALGORITHM FOR CONTINUOUS OPTIMIZATION

This paper presents a social harmony search algorithm to solve optimization problems with continuous design variables. Although the Harmony Search (HS) algorithm (HSA) has proven its ability in finding near global regions within a reasonable time, it is rather inefficient in performing local search. The proposed method applies the harmony search optimizer for global optimization and normal distribution is employed to update the position of each design variable of a new harmony found by the first rule of the HS (memory consideration) in every stage to rapidly attain the feasible solution space. Normal distribution works as a global search in early iterations and as a local search in final iterations to improve HS in order to quickly converge and find better solutions. Various benchmark optimization problems are used to illustrate the effectiveness and robustness of the proposed method. Finally, the experimental results reveal the superiority of the proposed method in quick convergence and finding better solutions compared to the classic HS, its recently developed variants, and some other optimization algorithms. Keywords– Social harmony search, normal distribution, meta-heuristics, optimization, diversification, intensification

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

[2]  Ali Kaveh,et al.  COST OPTIMIZATION OF A COMPOSITE FLOOR SYSTEM USING ANT COLONY SYSTEM , 2012 .

[3]  Bijaya K. Panigrahi,et al.  Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Zong Woo Geem,et al.  Novel derivative of harmony search algorithm for discrete design variables , 2008, Appl. Math. Comput..

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

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

[7]  Ali Kaveh,et al.  HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR THE FORCE METHOD-BASED SIMULTANEOUS ANALYSIS AND DESIGN , 2010 .

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

[9]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

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

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

[12]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .

[13]  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 .

[14]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

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

[16]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

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

[18]  Kwee-Bo Sim,et al.  Parameter-setting-free harmony search algorithm , 2010, Appl. Math. Comput..

[19]  Ali Kaveh,et al.  HARMONY SEARCH ALGORITHM FOR OPTIMUM DESIGN OF SLAB FORMWORK , 2010 .

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

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

[22]  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.

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

[24]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Zong Woo Geem,et al.  Music-Inspired Harmony Search Algorithm , 2009 .

[26]  Z. Geem.  Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

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

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

[29]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

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

[31]  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..

[32]  Yin-Fu Huang,et al.  Self-adaptive harmony search algorithm for optimization , 2010, Expert Syst. Appl..

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

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

[35]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[36]  M. P. Saka,et al.  Adaptive Harmony Search Method for Structural Optimization , 2010 .