An Improved Particle Swarm Optimization with Feasibility-Based Rules for Constrained Optimization Problems

This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules. A turbulence operator is incorporated into IPSO algorithm to overcome the premature convergence. At the same time, a set called FPS is proposed to save those P best locating in the feasible region. Different from the standard PSO, g best in IPSO is chosen from the FPS instead of the swarm. Furthermore, the mutation operation is applied to the P best with the maximal constraint violation value in the swarm, which can guide particles to close the feasible region quickly. The performance of IPSO algorithm is tested on a well-known benchmark suite and the experimental results show that the proposed approach is highly competitive, effective and efficient.

[1]  David Mautner Himmelblau,et al.  Applied Nonlinear Programming , 1972 .

[2]  Jeng-Shyang Pan,et al.  Intelligent Parallel Particle Swarm Optimization Algorithms , 2006, Parallel Evolutionary Computations.

[3]  Haiyan Lu,et al.  Self-adaptive velocity particle swarm optimization for solving constrained optimization problems , 2008, J. Glob. Optim..

[4]  Carlos A. Coello Coello,et al.  A constraint-handling mechanism for particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[6]  Pan Jeng-Shyang,et al.  Parallel Particle Swarm Optimization Algorithms with Adaptive Simulated Annealing , 2006 .

[7]  Michael N. Vrahatis,et al.  Particle Swarm Optimization Method for Constrained Optimization Problems , 2002 .

[8]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[9]  Angel Eduardo Muñoz Zavala,et al.  Constrained optimization via particle evolutionary swarm optimization algorithm (PESO) , 2005, GECCO '05.

[10]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[11]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.

[12]  Russell C. Eberhart,et al.  Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization , 2002 .

[13]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[14]  Ling Wang,et al.  A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization , 2007, Appl. Math. Comput..

[15]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[16]  Zbigniew Michalewicz,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[17]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[18]  Jeng-Shyang Pan,et al.  A Parallel Particle Swarm Optimization Algorithm with Communication Strategies , 2005, J. Inf. Sci. Eng..

[19]  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.

[20]  Panos M. Pardalos,et al.  A Collection of Test Problems for Constrained Global Optimization Algorithms , 1990, Lecture Notes in Computer Science.

[21]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization , 1999, Evolutionary Computation.

[22]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

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