An Adaptive Particle Swarm Optimization Using Hybrid Strategy

As an intelligent algorithm inspired by the foraging behavior in nature, particle swarm optimization (PSO) is famous for its few parameters, easy to implement and higher convergence accuracy. However, PSO also has a weakness over the local search, also called the prematurity, which resulted in the convergence accuracy reduced and the convergence speed slowed. For this, extremal optimization (EO), an excellent local search algorithm, has been introduced to be improved (CEO) and enhance the local search of PSO. Meanwhile, for improving its global search further, an improved opposition-based learning based on refraction principle (UOBL) has been chosen to enhance the global search of PSO, which is a better global optimization algorithm. In order to balance both of PSO to improve its optimization performance further, an adaptive hybrid PSO based on UOBL and CEO (AHOPSO-CEO) is proposed in this article. The large number of experiment results and analysis reveals that AHOPSO-CEO achieves better performance with other algorithms on the convergence speed and convergence accuracy for optimization problems.

[1]  Li Xiao,et al.  A DE and PSO based hybrid algorithm for dynamic optimization problems , 2014, Soft Comput..

[2]  Zhijian Wu,et al.  A New Approach of Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Adaptive Mutation , 2014, ICONIP.

[3]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[4]  Stefan Boettcher,et al.  Optimization with Extremal Dynamics , 2000, Complex..

[5]  Zhijian Wu,et al.  FIR digital filter design using improved particle swarm optimization based on refraction principle , 2017, Soft Comput..

[6]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[8]  Min-Rong Chen,et al.  A novel Artificial Bee Colony algorithm with integration of extremal optimization for numerical optimization problems , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[9]  Bo Yang,et al.  Improving particle swarm optimization using multi-layer searching strategy , 2014, Inf. Sci..

[10]  Xia Li,et al.  An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation , 2012, Inf. Sci..

[11]  Niusha Ghandehari,et al.  Hybrid Extremal Optimization and Glowworm Swarm Optimization , 2013 .

[12]  Rolf Wanka,et al.  Particle swarm optimization almost surely finds local optima , 2013, GECCO '13.

[13]  Gang Xu,et al.  On convergence analysis of particle swarm optimization algorithm , 2018, J. Comput. Appl. Math..

[14]  Yang Genke,et al.  Multiobjective extremal optimization with applications to engineering design , 2007 .

[15]  Peng Chen,et al.  Optimization with extremal dynamics for the traveling salesman problem , 2007 .

[16]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[17]  Yu-Wang Chen,et al.  Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling , 2008 .

[18]  Vahid Azadehgan,et al.  A new hybrid algorithm for optimization based on Artificial Bee Colony and Extremal Optimization , 2013, IEEE Conference Anthology.

[19]  Paczuski,et al.  Avalanche dynamics in evolution, growth, and depinning models. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[21]  Stefan Boettcher,et al.  Extremal optimization at the phase transition of the three-coloring problem. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[23]  Zhihua Cui,et al.  A Guaranteed Global Convergence Particle Swarm Optimizer , 2004, Rough Sets and Current Trends in Computing.

[24]  Ruhul A. Sarker,et al.  Self-adaptive mix of particle swarm methodologies for constrained optimization , 2014, Inf. Sci..

[25]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[26]  Xia Li,et al.  A novel particle swarm optimizer hybridized with extremal optimization , 2010, Appl. Soft Comput..