A NEW APPROACH TO GLOBAL OPTIMIZATION MOTIVATED BY PARLIAMENTARY POLITICAL COMPETITIONS

Several biology inspired optimization algorithms such as Genetic Algorithms, Ant Colony Optimization (ACO)and Particle Swarm Optimization (PSO) have previously been proposed by researchers. Recent approaches in numerical optimization have shifted to motivate from complex human social behaviors. In this paper, a new optimization algorithm, namely parliamentary optimization algorithm (POA) is proposed by studying the competitive and collaborative behaviors of political parties in a parliament. Experimental results reveal that our proposed approach is superior to PSO approach over some benchmark multidimensional functions.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[3]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[4]  A. Renn,et al.  Molecular computing: a review. I: Data and image storage , 1991 .

[5]  J. Linz.  The parliamentary system , 1993 .

[6]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[7]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

[9]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[10]  Mike Sharples,et al.  Socio-cognitive engineering: A methodology for the design of human-centred technology , 2002, European Journal of Operational Research.

[11]  New algorithms for some NP-optimization problems by DNA computing , 2002 .

[12]  Vincenzo Cutello,et al.  An Immunological Approach to Combinatorial Optimization Problems , 2002, IBERAMIA.

[13]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[14]  Albert A. Groenwold,et al.  A Study of Global Optimization Using Particle Swarms , 2005, J. Glob. Optim..

[15]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[16]  Mitsuo Gen,et al.  ADAPTIVE GENETIC ALGORITHMS FOR MULTI-RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM WITH MULTIPLE MODES , 2006 .

[17]  Z. Cui,et al.  A FAST PARTICLE SWARM OPTIMIZATION , 2006 .

[18]  Ning Wang,et al.  An Optimization Algorithm Inspired by Membrane Computing , 2006, ICNC.

[19]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[20]  Shu-Chuan Chu,et al.  COMPUTATIONAL INTELLIGENCE BASED ON THE BEHAVIOR OF CATS , 2007 .