Decentralized Control in Nature

15 Apr 2023 Advanced Logistics Concepts for Production and Supply 1

Decentralized Control in Nature for Production and Supply

Syed Shahzaib RazaMatriculation No. 16277


Contents

1. Introduction2. Swarm Intelligence3. Ant Colony Optimization4. Particle Swarm Optimization5. Artificial Bee Colony6. Firefly Algorithm7. Cuckoo Search8. SummaryDiscussion

Approximately 20 minutes

Approximately 10 minutes


Introduction

• Inspiration from nature• Behavior of living organisms• Social insects or animals like Ants, Birds or Fish• Collective intelligence of autonomous agents• Example: Flock of birds• Emergent behaviour


Swarm Intelligence

• Discipline of Artificial Intelligence• Meta-heuristic optimization techniques• Inspired from behaviour of social insects and

animals• Decentralized control, self-organization and

information sharing• Iterative procedure of algorithms


Ant Colony Optimization

• Introduced by Marco Dorigo in early 90s• Inspired from foraging behavior of ants• Prioritize colony survival• Indirect communication by using pheromone• Focus on Shortest Path Finding• Initially implemented in the field of

communications





• Application areas– Shortest path finding– Travelling salesman problem– Scheduling– Graph coloring– Routing in communication networks


Particle Swarm Optimization

• Proposed by Kennedy and Eberhart in 1995• Modeled on the behaviour of social animals

like flock of birds, school of fish etc.• Particles behave as individuals of a swarm• Update velocities and positions according to

neighbours• Focus on the area of search• Deliver quality solutions to the problems





• Applications– Industries– Transportation– Power systems– Location finding– Data mining


Artificial Bee Colony

• Introduced by Karaboga in 2005• Inspired by the behaviour of bees in colonies• Three main characteristics of honey bees• Focus on solution finding• Able to solve many complex problems





• Applications– Bioinformatics– Engineering design– Vehicle routing– Image processing– Data clustering– Economic dispatch problem


Firefly Algorithm

• Introduced by Yang in 2007• Inspired from behaviour of fireflies• Flashing light pattern• Movement towards brighter location• Process starts from lower light intensity• Comparison between the neighbours• Goal to achieve optimal solution


Firefly Algorithm


Firefly Algorithm

• Applications– Digital image processing– Image compression– Antenna design– Data analysis


Cuckoo Search

• Developed by Yang and Deb in 2009• Based on cuckoo species of birds• Aggressive reproduction strategy of cuckoo• One egg at a time• Search for the best nest of quality eggs• Finding the location by host bird• Provides set of solutions


Cuckoo Search


Cuckoo Search

• Applications– Pattern recognition– Job scheduling– Optimal path finding– Software testing– Networking– Business and health sectors


Summary

• SI based algorithms are nature inspired• Decentralization and Self-organization• Collective intelligence of agents• ACO, PSO, ABC, FA, CS algorithms• Different algorithms have different advantages• Able to solve complex problems in industry as

well as research• Application areas in production and logistics


References

1. Sadrnia et al., “A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems”, LASCIT International Journal of Engineering and Technology, vol. 6, No. 3, June 2014.

2. G. Beni, “The concept of cellular robotic systems”, In Proceedings of the IEEE International Symposium on Intelligent Systems, pg 57-62, IEEE Press, Piscataway, NJ, 1988.

3. Christian Blum and Xiaodong Li, “Swarm Intelligence in Optimization” 4. Marco Dorigo, “Optimization, learning and natural algorithms”, Ph. D. Thesis, Politecnico di Milano, Italy, 1992. 5. Yang Liu and Kevin M. Passino, “Swarm Intelligence: Literature Overview”, Dept. of Electrical Engineering, The Ohio

State University, March 30, 2000. 6. Pontus Svenson et al., “Swarm Intelligence for logistics: Background”, Swedish Defence Research Agency, February

2004. 7. James Kennedy and Russel Eberhart, “Particle swarm optimization”, In Neural Networks Proceedings of IEEE

International Conference, vol. 4, pages 1942-1948, IEEE, 1995. 8. Dervis Karaboga and Bahriye Basturk, “A powerful and efficient algorithm for numerical function optimization:

artificial bee colony (abc) algorithm”, Journal of global optimization, vol. 39(3), pages 459-471, 2007. 9. Sagar Tiwari et al., “Algorithms of Swarm Intelligence Using Data Clustering”, International Journal of Computer

Science and Information Technologies, vol. 4(4), pages 549-552, 2013. 10. X. S. Yang, “Swarm intelligence based algorithms: a critical analysis”, Evolutionary Intelligence, vol. 7, no. 1, pp. 17-28

(2014). 11. Xin-She Yang, “Firefly algorithm, stochastics test functions and design optimization”, International Journal of Bio-

Inspired Computation, vol. 2(2), pages 78-84, 2010. 12. Ajith Abraham et al., “Swarm Intelligence Algorithms for Data Clustering”


Discussion


Thank you