Porcellio scaber algorithm (PSA) for solving constrained optimization problems

In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.

[1]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[2]  Shuai Li,et al.  Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators , 2016, IEEE Transactions on Industrial Electronics.

[3]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[4]  Ong Pauline,et al.  Design optimization of structural engineering problems using adaptive cuckoo search algorithm , 2017, 2017 3rd International Conference on Control, Automation and Robotics (ICCAR).

[5]  Jung-Fa Tsai,et al.  Global optimization for signomial discrete programming problems in engineering design , 2002 .

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

[7]  S. Wu,et al.  GENETIC ALGORITHMS FOR NONLINEAR MIXED DISCRETE-INTEGER OPTIMIZATION PROBLEMS VIA META-GENETIC PARAMETER OPTIMIZATION , 1995 .

[8]  Long Jin,et al.  Taylor $O(h^{3})$ Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Shuai Li,et al.  Zeroing neural networks: A survey , 2017, Neurocomputing.

[10]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[11]  Amir Hossein Gandomi,et al.  Benchmark Problems in Structural Optimization , 2011, Computational Optimization, Methods and Algorithms.

[12]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

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

[14]  Siamak Talatahari,et al.  An improved ant colony optimization for constrained engineering design problems , 2010 .

[15]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[16]  George G. Dimopoulos,et al.  Mixed-variable engineering optimization based on evolutionary and social metaphors , 2007 .

[17]  Mahamed G. H. Omran,et al.  Constrained optimization using CODEQ , 2009 .

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

[19]  Long Jin,et al.  Continuous and discrete Zhang dynamics for real-time varying nonlinear optimization , 2015, Numerical Algorithms.

[20]  Tapabrata Ray,et al.  A socio-behavioural simulation model for engineering design optimization , 2002 .

[21]  Han-Lin Li,et al.  A GLOBAL APPROACH FOR NONLINEAR MIXED DISCRETE PROGRAMMING IN DESIGN OPTIMIZATION , 1993 .

[22]  Kusum Deep,et al.  Effectiveness of Constrained Laplacian Biogeography Based Optimization for Solving Structural Engineering Design Problems , 2016, SocProS.

[23]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[24]  Carlos A. Coello Coello,et al.  Hybridizing a genetic algorithm with an artificial immune system for global optimization , 2004 .

[25]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[26]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .

[27]  Long Jin,et al.  Discrete-Time Zhang Neural Network for Online Time-Varying Nonlinear Optimization With Application to Manipulator Motion Generation , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[29]  Shuai Li,et al.  PSA: A Novel Optimization Algorithm Based on Survival Rules of Porcellio Scaber , 2017, 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).