PSA: A Novel Optimization Algorithm Based on Survival Rules of Porcellio Scaber

Bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we design and propose an algorithm called the porcellio scaber algorithm (PSA) for solving optimization problems, including differentiable and non-differential ones as well as the case with local optimums. Numerical results based on benchmark problems are presented to validate the efficacy of PSA.

[1]  Jemal H. Abawajy,et al.  An efficient meta-heuristic algorithm for grid computing , 2013, Journal of Combinatorial Optimization.

[2]  Rajkumar Buyya,et al.  BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources , 2018, Journal of Network and Systems Management.

[3]  Zhitao Lin,et al.  Relative ordering learning in spiking neural network for pattern recognition , 2018, Neurocomputing.

[4]  J. F. Price,et al.  On descent from local minima , 1971 .

[5]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[6]  Shuai Li,et al.  Porcellio scaber algorithm (PSA) for solving constrained optimization problems , 2017, ArXiv.

[7]  Guo-Xing Wen,et al.  Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems , 2014, IEEE Transactions on Cybernetics.

[8]  Shahryar Rahnamayan,et al.  A novel population initialization method for accelerating evolutionary algorithms , 2007, Comput. Math. Appl..

[9]  Mark Pogson,et al.  Simulation of Invertebrate Aggregation Shows the Importance of Stable Personality over Diversity in Consensus Decision-Making , 2016, PloS one.

[10]  Amir Hossein Gandomi,et al.  Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect , 2012, Appl. Soft Comput..

[11]  Shuai Li,et al.  Distributed Task Allocation of Multiple Robots: A Control Perspective , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Ajith Abraham,et al.  Ideology algorithm: a socio-inspired optimization methodology , 2017, Neural Computing and Applications.

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

[14]  J.-L. Deneubourg,et al.  Benefits of aggregation in woodlice: a factor in the terrestrialization process? , 2013, Insectes Sociaux.

[15]  Neculai Andrei,et al.  An adaptive scaled BFGS method for unconstrained optimization , 2018, Numerical Algorithms.

[16]  Robert G. Reynolds,et al.  An improved class of real-coded Genetic Algorithms for numerical optimization✰ , 2018, Neurocomputing.

[17]  K. E. Linsenmair,et al.  Comparative studies on the social behaviour of the desert isopod Hemilepistus reaumuri and of a Porcellio species , 1984 .

[18]  Mark Hassall,et al.  Sheltering behavior of terrestrial isopods in grasslands , 2007 .