Gastropod Mollusc (or Slug) Optimisation Algorithm Technical Report on Optimization Techniques for Problem Solving in Uncertainty

This chapter presents a nature–inspired computing optimisation algorithm. The computational algorithm is based upon the patterns and behaviours of the extraordinary and under–appreciated Gastropod Mollusc (or Slug). The slug which has been around since the ice–age, belongs to a fascinating and complex group of creatures whose biology is every bit as interesting and worthy of admiration as Earth’s more loved and head line grabbing species. As we explain in this chapter, slugs are simple creatures but are able to solve complex problems in large groups (one of nature’s evolutionary triumphs). These abilities form the underpinnings of the ‘slug optimisation algorithm’ (SOA) presented in this chapter. What is more, the optimisation algorithm is scalable and can be implemented on massively parallel architectures (like the graphical processing unit). While algorithms, such as, the firefly, cockroach, and bee, have proven themselves as efficient methods for finding optimal solutions to complex problems, we hope after reading this chapter the reader will take a similar view on the slug optimisation algorithm.

[1]  A. Gelperin.  Olfactory Basis of Homing Behavior in the Giant Garden Slug, Limax maximus. , 1974, Proceedings of the National Academy of Sciences of the United States of America.

[2]  R. Menzel,et al.  Biology of Invertebrate Learning , 1984 .

[3]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[4]  Deniz Yuret,et al.  Dynamic Hill Climbing: Overcoming the limitations of optimization techniques , 1993 .

[5]  James P. Kelly,et al.  Meta-Heuristics: An Overview , 1996 .

[6]  Jörg Fliege,et al.  Steepest descent methods for multicriteria optimization , 2000, Math. Methods Oper. Res..

[7]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[10]  Tien-Tsin Wong,et al.  Parallel evolutionary algorithms on graphics processing unit , 2005, 2005 IEEE Congress on Evolutionary Computation.

[11]  Tien-Tsin Wong,et al.  Parallel Hybrid Genetic Algorithms on Consumer-Level Graphics Hardware , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Jean-Philippe Rennard,et al.  Stochastic Optimization Algorithms , 2007, ArXiv.

[13]  Lee W. Howes.  Efficient Random Number Generation and Application Using , 2007 .

[14]  Wen-mei W. Hwu,et al.  Optimization principles and application performance evaluation of a multithreaded GPU using CUDA , 2008, PPoPP.

[15]  Rhyd Lewis,et al.  A survey of metaheuristic-based techniques for University Timetabling problems , 2007, OR Spectr..

[16]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[17]  A. Hosoi,et al.  Nonlinear viscoelastic biomaterials: meaningful characterization and engineering inspiration. , 2009, Integrative and comparative biology.

[18]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[19]  Jianwen Zhu,et al.  Parallelizing Simulated Annealing-Based Placement Using GPGPU , 2010, 2010 International Conference on Field Programmable Logic and Applications.

[20]  Kalyanmoy Deb,et al.  Parallelization of binary and real-coded genetic algorithms on GPU using CUDA , 2010, IEEE Congress on Evolutionary Computation.

[21]  Xin-She Yang,et al.  Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning , 2011, Int. J. Swarm Intell. Res..

[22]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

[23]  Benjamin Kenwright,et al.  Planar character animation using genetic algorithms and GPU parallel computing , 2014, Entertain. Comput..

[24]  Ben Kenwright.  Inverse Kinematic Solutions for Articulated Characters using Massively Parallel Architectures and Differential Evolutionary Algorithms , 2017, VRIPHYS.