A Multi-Objective Binary Harmony Search Algorithm

Harmony Search (HS) is an emerging meta-heuristic optimization method and has been used to tackle various optimization problems successfully. However, the research of multi-objectives HS just begins and no work on binary multi-objectives HS has been reported. This paper presents a multi-objective binary harmony search algorithm (MBHS) for tackling binary-coded multiobjective optimization problems. A modified pitch adjustment operator is used to improve the search ability of MBHS. In addition, the non-dominated sorting based crowding distance is adopted to evaluate the solution and update the harmony memory to maintain the diversity of algorithm. Finally the performance of the proposed MBHS was compared with NSGA-II on multi-objective benchmark functions. The experimental results show that MBHS outperform NSGA-II in terms of the convergence metric and the diversity metric.

[1]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

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

[3]  Z W Geem,et al.  APPLICATION OF HARMONY SEARCH TO MULTI-OBJECTIVE OPTIMIZATION FOR SATELLITE HEAT PIPE DESIGN , 2006 .

[4]  Li Li,et al.  A Novel Hybrid Real-Valued Genetic Algorithm for Optimization Problems , 2007 .

[5]  Woo-seok Jang,et al.  Hybrid Simplex-Harmony search method for optimization problems , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[6]  Zong Woo Geem,et al.  Harmony Search in Water Pump Switching Problem , 2005, ICNC.

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

[8]  Zong Woo Geem,et al.  Multiobjective Optimization of Time-Cost Trade-Off Using Harmony Search , 2010 .

[9]  Yew-Soon Ong,et al.  Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I , 2005, ICNC.

[10]  Q. H. Wu,et al.  A heuristic particle swarm optimizer for optimization of pin connected structures , 2007 .

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

[12]  Xiangfei Nie,et al.  Intrinsic Dimensionality Estimation with Neighborhood Convex Hull , 2007 .

[13]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

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

[15]  Xiao Zhi Gao,et al.  A Hybrid Optimization Method for Fuzzy Classification Systems , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[16]  Jing J. Liang,et al.  A self-adaptive global best harmony search algorithm for continuous optimization problems , 2010, Appl. Math. Comput..

[17]  Minrui Fei,et al.  A Discrete Harmony Search Algorithm , 2010 .

[18]  Jerzy Kotowski,et al.  Analysis of the Properties of the Harmony Search Algorithm Carried Out on the One Dimensional Binary Knapsack Problem , 2009, EUROCAST.

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