A self-adaptive global best harmony search algorithm for continuous optimization problems

This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.

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

[2]  Abolfazl Toroghi Haghighat,et al.  Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing , 2008, Comput. Commun..

[3]  S. O. Degertekin Harmony search algorithm for optimum design of steel frame structures: A comparative study with other optimization methods , 2008 .

[4]  M. Fesanghary,et al.  Combined heat and power economic dispatch by harmony search algorithm , 2007 .

[5]  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.

[6]  M. Tamer Ayvaz,et al.  Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm , 2007 .

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

[8]  K. Lee,et al.  The harmony search heuristic algorithm for discrete structural optimization , 2005 .

[9]  M. Mahdavi,et al.  Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems , 2008 .

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

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

[12]  Halim Ceylan,et al.  Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .


[14]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[15]  Zong Woo Geem,et al.  Application of Harmony Search to Vehicle Routing , 2005 .

[16]  Z. Geem Particle-swarm harmony search for water network design , 2009 .

[17]  Zong Woo Geem,et al.  Harmony Search Optimization: Application to Pipe Network Design , 2002 .

[18]  S. O. Degertekin Optimum design of steel frames using harmony search algorithm , 2008 .

[19]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

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

[21]  Zong Woo Geem Harmony search optimisation to the pump-included water distribution network design , 2009 .

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

[23]  Z. Geem Optimal Design of Water Distribution Networks Using Harmony Search , 2009 .