BCLO - Brainstorming and Collaborative Learning Optimization Algorithms

Brainstorming Optimization (BSO) algorithms are considered as one of the variations of swarm intelligence. Brainstorming optimization concept is based on a human being thinking and intelligence in solving complex problems. BSO basically emulates the human brain functionality in dealing with different situations. There are many techniques are already used in educating people and they prove their effectiveness. This chapter is a step towards explaining the main concept behind swarm intelligence. It goes over the swarm intelligence in business, routing algorithms, and in optimization. Then, it explains the main idea behind the concept of brainstorming optimization. It elaborates on brainstorming techniques and their variations including Fuzzy-brainstorming optimization. Moreover, this chapter introduces three novel optimization algorithms that are motivated from the collaborative learning approaches used in education. It presents Think-and-Share Optimization (TaSO), Think-Pair-Square Optimization (TPSO), and R-Parallel-Collaborative Optimizations (RPCO) Algorithm.

[1]  M. Smith,et al.  Key Connections: The U.S. Department of Energy?s Microgrid Initiative , 2013 .

[2]  R. Misra,et al.  Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks , 2006, 2006 IFIP International Conference on Wireless and Optical Communications Networks.

[3]  David W. Johnson,et al.  Cooperative learning : increasing college faculty instructional productivity , 1991 .

[4]  M.A. El-Sharkawi,et al.  Swarm intelligence for routing in communication networks , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[5]  G. Griggs,et al.  Researching the pieces of a puzzle: the use of a jigsaw learning approach in the delivery of undergraduate gymnastics , 2010 .

[6]  Torsten Braun,et al.  A Framework for Routing in Large Ad-hoc Networks with Irregular Topologies , 2006, Ad Hoc Sens. Wirel. Networks.

[7]  Jianguo Sun,et al.  Lightlike Hypersurfaces and Canal Hypersurfaces of Lorentzian Surfaces , 2014 .

[8]  Mohamed A. Moustafa Hassan,et al.  Application of Particle Swarm Optimization in Design of PID Controller for AVR System , 2013, Int. J. Syst. Dyn. Appl..

[9]  Bijaya K. Panigrahi,et al.  Brain Storming Incorporated Teaching-Learning-Based Algorithm with Application to Electric Power Dispatch , 2012, SEMCCO.

[10]  Yanqiu Sun,et al.  A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting , 2014 .

[11]  Muddassar Farooq,et al.  Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions , 2011, Inf. Sci..

[12]  Luca Maria Gambardella,et al.  Principles and applications of swarm intelligence for adaptive routing in telecommunications networks , 2010, Swarm Intelligence.

[13]  Gurdip Singh,et al.  Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks , 2005 .

[14]  Rabie A. Ramadan,et al.  Efficient Data Reporting in a Multi-Object Tracking Using WSNs , 2017, Int. J. Syst. Dyn. Appl..

[15]  Ying Zhang,et al.  Improvements on Ant Routing for Sensor Networks , 2004, ANTS Workshop.

[16]  Rajendra V. Boppana,et al.  An adaptive distance vector routing algorithm for mobile, ad hoc networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[17]  Yu Zhang,et al.  ACO Based QoS Routing Algorithm for Wireless Sensor Networks , 2006, UIC.

[18]  Mohammad S. Obaidat,et al.  A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks , 2008 .

[19]  Xiaodong Li,et al.  Swarm Intelligence in Optimization , 2008, Swarm Intelligence.

[20]  E Bonabeau,et al.  Swarm Intelligence: A Whole New Way to Think about Business , 2001 .

[21]  Ahmed M. Anter,et al.  Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation , 2018, J. Comput. Sci..

[22]  Rabie A. Ramadan,et al.  Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System , 2016, Int. J. Syst. Dyn. Appl..

[23]  Aboul Ella Hassanien,et al.  Binary ant lion approaches for feature selection , 2016, Neurocomputing.

[24]  Rabie A. Ramadan.  Fuzzy brain storming optimisation algorithm , 2017, Int. J. Intell. Eng. Informatics.

[25]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

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

[27]  Yi Zhou,et al.  A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy , 2013, Int. J. Syst. Dyn. Appl..

[28]  David W. Johnson,et al.  Active Learning: Cooperation in the College Classroom , 2006 .

[29]  Guy Theraulaz,et al.  The biological principles of swarm intelligence , 2007, Swarm Intelligence.

[30]  R. Slavin.  Cooperative Learning, Success for All, and Evidence-based Reform in education , 2008 .

[31]  Han-Chieh Chao,et al.  Jumping ant routing algorithm for sensor networks , 2007, Comput. Commun..

[32]  Bijaya K. Panigrahi,et al.  Optimal Power Flow Solution Using Self-Evolving Brain-Storming Inclusive Teaching-Learning-Based Algorithm , 2013, ICSI.

[33]  Abu Saleh Md. Mahfujur Rahman,et al.  Ant colony-based many-to-one sensory data routing in Wireless Sensor Networks , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[34]  Shahram Jamali,et al.  Routing Algorithm for Vehicular Ad Hoc Network Based on Dynamic Ant Colony Optimization , 2016 .

[35]  Liu Liang,et al.  ASAR: An ant-based service-aware routing algorithm for multimedia sensor networks , 2008 .

[36]  Diana Hulse-killacky,et al.  A Model for Collaborative Teaching Teams in Counselor Education. , 2008 .

[37]  Niannian Ding,et al.  Data Gathering Communication in Wireless Sensor Networks Using Ant Colony Optimization , 2004, ROBIO.

[38]  Muddassar Farooq,et al.  BeeSensor: A Bee-Inspired Power Aware Routing Protocol for Wireless Sensor Networks , 2009, EvoWorkshops.

[39]  Yuhui Shi,et al.  Predator–Prey Brain Storm Optimization for DC Brushless Motor , 2013, IEEE Transactions on Magnetics.

[40]  Shehzad Khalid,et al.  Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions , 2013, Int. J. Distributed Sens. Networks.

[41]  Min Pan,et al.  Adaptive ant-based routing in wireless sensor networks using Energy*Delay metrics , 2008 .

[42]  Chien-Chung Shen,et al.  ANSI: A swarm intelligence-based unicast routing protocol for hybrid ad hoc networks , 2006, J. Syst. Archit..

[43]  Yuhui Shi,et al.  Brain storm optimization algorithm in objective space , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[44]  Masayuki Murata,et al.  Self-Organized Data-Gathering Scheme for Multi-Sink Sensor Networks Inspired by Swarm Intelligence , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).