Genetic Algorithm, Particle Swarm Optimization and Harmony Search: A quick comparison

There exists several complex optimization problems, are difficult to solve using simple conventional or mathematical approach. Many scientific applications have a search space exponentially proportional to the problem dimensions, cannot be solved employing exhaustive search methods. Therefore, there is considerable interest in metaheuristic methods attempt to discover near optimal solution within the acceptable time. This paper presents a comprehensive study and comparison of three: Genetic Algorithm, Particle Swarm Optimization and Harmony Search, global optimization algorithms. The comparative analysis has been reported in an organized manner for quick review. The underlying motivation is to identify possibility to develop a new hybrid algorithm to solve real world problems.

[1]  Ankit Chaudhary,et al.  Grammar induction using bit masking oriented genetic algorithm and comparative analysis , 2016, Appl. Soft Comput..

[2]  Nitin S. Choubey,et al.  Developing Genetic Algorithm Library Using Java for CFG Induction , 1970 .

[3]  Zhihua Cui,et al.  Integral Particle Swarm Optimization with Dispersed Accelerator Information , 2009, Fundam. Informaticae.

[4]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[5]  Dennis Weyland,et al.  A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a "Novel" Methodology , 2010, Int. J. Appl. Metaheuristic Comput..

[6]  WeylandDennis.  A Rigorous Analysis of the Harmony Search Algorithm , 2010 .

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

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

[9]  Qun Niu,et al.  A hybrid binary harmony search algorithm inspired by ant system , 2011, 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS).

[10]  Ajith Abraham,et al.  An Improved Harmony Search Algorithm with Differential Mutation Operator , 2009, Fundam. Informaticae.

[11]  Xin-She Yang.  Harmony Search as a Metaheuristic Algorithm , 2009 .

[12]  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.

[13]  Ankit Chaudhary,et al.  A comparative review of approaches to prevent premature convergence in GA , 2014, Appl. Soft Comput..

[14]  Zhihua Cui,et al.  PID-Controlled Particle Swarm Optimization , 2010, J. Multiple Valued Log. Soft Comput..

[15]  Deepti Mehrotra,et al.  Genetic algorithms: concepts, issues and a case study of grammar induction , 2012, CUBE.

[16]  Hari Mohan Pandey.  Context free grammar induction library using Genetic Algorithms , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[17]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..