Attractive and Repulsive Fully Informed Particle Swarm Optimization based on the modified Fitness Model

A novel Attractive and Repulsive Fully Informed Particle Swarm Optimization based on the modified Fitness Model (ARFIPSOMF) is presented. In ARFIPSOMF, a modified fitness model is used as a self-organizing population structure construction mechanism. The population structure is gradually generated as the construction and the optimization processes progress asynchronously. An attractive and repulsive interacting mechanism is also introduced. The cognitive and the social effects on each particle are distributed by its ‘contextual fitness’ value $$F$$F. Two kinds of experiments are conducted. Results focusing on the optimization performance show that the proposed algorithm maintains stronger diversity of the population during the convergent process, resulting in good solution quality on a wide range of test functions, and converge faster. Moreover, the results concerning on topologic characteristics of the population structure indicate that (1) the final population structures developed by optimizing different test functions differ, which is an important for improving ARFIPSOMF performance, and (2) the final structures developed by optimizing some test functions exhibit scale-free property approximately.

[1]  Chenggong Zhang,et al.  Scale-free fully informed particle swarm optimization algorithm , 2011, Inf. Sci..

[2]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[4]  Robert L. Stewart,et al.  An analysis of the effects of population structure on scalable multiobjective optimization problems , 2007, GECCO '07.

[5]  Suranga Hettiarachchi,et al.  An Overview of Physicomimetics , 2004, Swarm Robotics.

[6]  Ruhul A. Sarker,et al.  The Self-Organization of Interaction Networks for Nature-Inspired Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[7]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[8]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[9]  Yunlong Zhu,et al.  An Improved Particle Swarm Optimization Based on Bacterial Chemotaxis , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[10]  Jianchao Zeng,et al.  Particle swarm optimisation based on self-organising topology driven by fitness with different links removing strategies , 2012 .

[11]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  Oscar Castillo,et al.  Design of optimal membership functions for fuzzy controllers of the water tank and inverted pendulum with PSO variants , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[13]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[14]  Marco Tomassini,et al.  Effects of Scale-Free and Small-World Topologies on Binary Coded Self-adaptive CEA , 2006, EvoCOP.

[15]  Robert L. Stewart,et al.  Multiobjective Evolutionary Algorithms on Complex Networks , 2006, EMO.

[16]  Oscar Castillo,et al.  Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic , 2013, Expert Syst. Appl..

[17]  Yu-Xuan Wang,et al.  Particle Swarms with dynamic ring topology , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[18]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[19]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[20]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[21]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[22]  Ernesto Costa,et al.  An Empirical Comparison of Particle Swarm and Predator Prey Optimisation , 2002, AICS.

[23]  Oscar Castillo,et al.  A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation , 2014, Expert Syst. Appl..

[24]  Ginestra Bianconi,et al.  Competition and multiscaling in evolving networks , 2001 .

[25]  Mohammed El-Abd,et al.  Information exchange in multiple cooperating swarms , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..