A novel constrained bare-bones particle swarm optimization

Particle swarm optimization (PSO) has been applied to nonlinear constrained problems. However, PSO may easily get trapped in the local optima when solving complex problems and suffers from the setting of learning parameters. In order to improve convergence accuracy of solutions, a hierarchical learning bare-bones PSO is proposed, named HLBPSO, for dealing with constrained optimization problems. HLBPSO adopts a hierarchical learning strategy to maintain population diversity. In HLBPSO, an archive is used to store the accepted infeasible solutions and auxiliary operations are introduced to help accepted infeasible solutions to enter into the feasible region. Experiments were conducted on constrained benchmark problems. The experimental results showed that HLBPSO performs better than four other related works.

[1]  Tetsuyuki Takahama,et al.  Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation , 2010, IEEE Congress on Evolutionary Computation.

[2]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[3]  Efren Mezura-Montesand,et al.  Empirical Analysis of a Modified Artificial Bee Colony for Constrained Numerical Optimization , 2012 .

[4]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  Ruhul A. Sarker,et al.  Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..

[6]  A. Rezaee Jordehi,et al.  A review on constraint handling strategies in particle swarm optimisation , 2015, Neural Computing and Applications.

[7]  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.

[8]  Efrén Mezura-Montes,et al.  Empirical analysis of a modified Artificial Bee Colony for constrained numerical optimization , 2012, Appl. Math. Comput..

[9]  Andries Petrus Engelbrecht,et al.  A Convergence Proof for the Particle Swarm Optimiser , 2010, Fundam. Informaticae.

[10]  Sung Nam Jung,et al.  Advanced particle swarm assisted genetic algorithm for constrained optimization problems , 2014, Computational Optimization and Applications.

[11]  Tim Blackwell,et al.  A Study of Collapse in Bare Bones Particle Swarm Optimization , 2012, IEEE Transactions on Evolutionary Computation.

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

[13]  Hui Wang,et al.  Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems , 2012 .

[14]  Efrén Mezura-Montes,et al.  Improved Particle Swarm Optimization in Constrained Numerical Search Spaces , 2009, Nature-Inspired Algorithms for Optimisation.

[15]  Devid Desfreed Kennedy,et al.  Novel bare-bones particle swarm optimization and its performance for modeling vapor–liquid equilibrium data , 2011 .

[16]  Shaohua Xu,et al.  A Modified Quantum-Behaved Particle Swarm Optimization for Constrained Optimization , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[17]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.