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Difference between revisions of "A Brief Introduction to Swarm Intelligence"

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==Intellectus (Nous)==
 
==Intellectus (Nous)==
  
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<center>[[File:intellectus.png]]</center>
 
<center>[[File:intellectus.png]]</center>
<center>{{Figure|'''Source:''' Edward Grant, "Celestial Orbs in the Latin Middle Ages", Isis, Vol. 78, No. 2. (Jun., 1987), pp. 152-173.}}</center>
+
<center>{{Figure|'''Credit:''' E. Grant<ref>Edward Grant, "Celestial Orbs in the Latin Middle Ages", Isis, Vol. 78, No. 2. (Jun., 1987), pp. 152-173.</ref>.}}</center>
 
</div>
 
</div>
 
</div>
 
</div>
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Brain-to-body mass ratio
 
Brain-to-body mass ratio
 
 
</div>
 
</div>
 
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<div class="column">
 
<center>[[File:nocturnal-squid.jpg]]</center>
 
<center>[[File:nocturnal-squid.jpg]]</center>
<center>{{Figure|'''Source:''' https://commons.wikimedia.org/wiki/User:Nhobgood}}</center>
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<center>{{Figure|'''Credit:''' Wikimedia Commons, Nhobgood<ref>[https://commons.wikimedia.org/wiki/User:Nhobgood Wikimedia Commons, Nhobgood].</ref>.}}</center>
 
</div>
 
</div>
  
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==42==
 
==42==
  
In the radio series and the first novel, a group of hyper-in- telligent pan-dimensional beings demand to learn the
+
In the radio series and the first novel, a group of hyper-intelligent pan-dimensional beings demand to learn the
 
Answer to the Ultimate Question of Life, The Universe, and Everything
 
Answer to the Ultimate Question of Life, The Universe, and Everything
from the supercomputer, Deep Thought, specially built for this purpose. It takes Deep Thought 71⁄2 million years to compute and check the answer, which turns out to be 42. Deep Thought points out that the answer seems mean- ingless because the beings who instructed it never actually knew what the Question was.
+
from the supercomputer, Deep Thought, specially built for this purpose. It takes Deep Thought 71⁄2 million years to compute and check the answer, which turns out to be 42. Deep Thought points out that the answer seems meaningless because the beings who instructed it never actually knew what the Question was.
 
The Ultimate Question:
 
The Ultimate Question:
 
What do you get if you multiply six by nine?
 
What do you get if you multiply six by nine?
Line 142: Line 140:
 
==Swarm Intelligence==
 
==Swarm Intelligence==
  
Swarm intelligence (SI) is the collective behavior of decentralized, self-orga- nized systems, natural or artificial. Introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellu- lar robotic systems.
+
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. Introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
 
Examples in natural systems of SI:
 
Examples in natural systems of SI:
 
• Ant colonies
 
• Ant colonies
Line 161: Line 159:
  
 
Nature Inspired Search Techniques
 
Nature Inspired Search Techniques
Particle swarm optimization
 
Simulating social behaviour.
 
• Kennedy, J.; Eberhart, R. (1995). “Particle Swarm Optimi- zation”. Proceedings of IEEE International Conference on Neural Networks IV. pp. 1942–1948.
 
• Shi, Y.; Eberhart, R.C. (1998). “A modified particle swarm optimizer”. Proceedings of IEEE International Confer- ence on Evolutionary Computation. pp. 69–73.
 
• Kennedy, J. (1997). “The particle swarm: social adapta- tion of knowledge”. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 303– 308.
 
  
 +
<div class="columns">
 +
<div class="column">
 +
''Particle swarm optimization.'' Simulating social behaviour.
 +
* Kennedy, J.; Eberhart, R. (1995). “Particle Swarm Optimization”. Proceedings of IEEE International Conference on Neural Networks IV. pp. 1942–1948.
 +
* Shi, Y.; Eberhart, R.C. (1998). “A modified particle swarm optimizer”. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
 +
* Kennedy, J. (1997). “The particle swarm: social adaptation of knowledge”. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 303– 308.
 +
</div>
 +
<div class="column">
 
<center>[[File:psi-example.png]]</center>
 
<center>[[File:psi-example.png]]</center>
<center>{{Figure|'''Source:''' Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group.}}</center>
+
<center>{{Figure|'''Credit:''' M. E. H. Pedersen<ref>Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group.</ref>.}}</center>
 +
</div>
 +
</div>
  
Ant colony optimization
+
<div class="columns">
A probabilistic technique in metaheuristic optimizations.
+
<div class="column">
A. Colorni, M. Dorigo et V. Maniezzo, Distributed Op- timization by Ant Colonies, actes de la première con- férence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
+
''Ant colony optimization.'' A probabilistic technique in metaheuristic optimizations.
M. Dorigo, Optimization, Learning and Natural Algo- rithms, PhD thesis, Politecnico di Milano, Italy, 1992.
+
* A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
 +
* M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italy, 1992.
 +
</div>
 +
<div class="column">
 +
<center>[[File:aco-example.png]]</center>
 +
<center>{{Figure|'''Credit:''' Wikimedia Commons<ref>[https://commons.wikimedia.org/wiki/File:Aco_shortpath.svg Wikimedia Commons].</ref>.}}</center>
 +
</div>
 +
</div>
  
<center>[[File:aco-example.png]]</center>
+
''Artificial bee colony algorithm.'' Intelligent foraging behaviour.
<center>{{Figure|'''Source:''' Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group.}}</center>
+
* D. Dervis Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005.
  
Artificial bee colony algorithm
 
Intelligent foraging behaviour.
 
• D. Dervis Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Re- port-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005.
 
 
Multi-level thresholding
 
Multi-level thresholding
 +
 
MR brain image classification Face pose estimation
 
MR brain image classification Face pose estimation
  
Differential evolution
+
''Differential evolution.'' A method that optimizes a problem by iteratively trying to improve a candidate solution.
A method that optimizes a problem by iteratively trying to improve a candidate solution.
+
* Storn, R.; Price, K. (1997). “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces”. Journal of Global Optimization 11: 341–359..
Storn, R.; Price, K. (1997). “Differential evolution - a sim- ple and efficient heuristic for global optimization over continuous spaces”. Journal of Global Optimization 11: 341–359..
+
* Storn, R. (1996). “On the usage of differential evolution for function optimization”. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS). pp. 519–523.
Storn, R. (1996). “On the usage of differential evolution for function optimization”. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS). pp. 519–523.
+
 
Parallel computing Multiobjective optimization Constrained optimization
+
Parallel computing
 +
 
 +
Multi-objective optimization
 +
 
 +
Constrained optimization
 +
 
 +
''The bees algorithm.'' A population-based search algorithm.
 +
* Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
 +
* Pham, D.T., Castellani, M. (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
 +
* Pham, D.T. and Castellani, M. (2013), Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms, Soft Computing, 1-33.
 +
 
 +
Optimisation of classifiers/Clustering systems
 +
 
 +
Manufacturing
  
The bees algorithm
 
A population-based search algorithm
 
• Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manu- facturing Engineering Centre, Cardiff University, UK, 2005.
 
• Pham, D.T., Castellani, M. (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
 
• Pham, D.T. and Castellani, M. (2013), Benchmarking and Comparison of Nature-Inspired Population-Based Con- tinuous Optimisation Algorithms, Soft Computing, 1-33.
 
Optimisation of classifiers/Clustering systems Manufacturing
 
 
Bioengineering
 
Bioengineering
 +
 
Multi-objective optimization
 
Multi-objective optimization
  
Artificial immune systems
+
''Artificial immune systems.'' A class of computationally intelligent systems. Adaptive systems.
A class of computationally intelligent systems. Adaptive systems.
+
* J.D. Farmer, N. Packard and A. Perelson, (1986) “The immune system, adaptation and machine learning”, Physica D, vol. 2, pp. 187–204.
+
 
J.D. Farmer, N. Packard and A. Perelson, (1986) “The im- mune system, adaptation and machine learning”, Physica D, vol. 2, pp. 187–204.
 
 
Bioinformatics
 
Bioinformatics
  
Bat algorithm
+
''Bat algorithm.'' A metaheuristic optimization algorithm.
A metaheuristic optimization algorithm.
+
* X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).
X. S. Yang, A New Metaheuristic Bat-Inspired Algo- rithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).
+
 
 
Engineering design
 
Engineering design
 +
 
Classifications of gene expression data
 
Classifications of gene expression data
  
Glowworm swarm optimization
+
''Glowworm swarm optimization.'' The algorithm makes the agents glow at intensities approximately proportional to the function value being optimized.
The algorithm makes the agents glow at intensities ap- proximately proportional to the function value being opti- mized.
+
* K.N. Krishnanand and D. Ghose (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84- 91.
K.N. Krishnanand and D. Ghose (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84- 91.
+
* K.N. Krishnanand and D. Ghose. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3):209- 222.
K.N. Krishnanand and D. Ghose. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multi- agent and Grid Systems, 2(3):209- 222.
+
* K.N. Krishnanand and D. Ghose. (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2):87- 124.
K.N. Krishnanand and D. Ghose. (2009). Glowworm swarm optimization for simultaneous capture of multi- ple local optima of multimodal functions. Swarm Intelli- gence, 3(2):87- 124.
+
* K.N. Krishnanand and D. Ghose. (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7):549- 569.
K.N. Krishnanand and D. Ghose. (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Auton- omous Systems, 56(7):549- 569.
+
 
 +
''Gravitational search algorithm.'' Based on the law of gravity and the notion of mass interactions.
 +
* Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S. (2009). “GSA: a gravitational search algorithm”. Information Science 179 (13): 2232–2248.
 +
 
 +
''Self-propelled particles.'' Predict robust emergent behaviours occur in swarms independent of the type of animal that is in the swarm.
 +
* Buhl, J.; Sumpter, D. J. T.; Couzin, D.; Hale, J. J.; Despland, E.; Miller, E. R.; Simpson, S. J. (2006). “From disorder to order in marching locusts” (PDF). Science 312 (5778): 1402–1406.
  
Gravitational search algorithm
+
''Stochastic diffusion search.'' An agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions.
Based on the law of gravity and the notion of mass interac- tions.
 
• Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S. (2009). “GSA: a gravitational search algorithm”. Information Science 179 (13): 2232–2248.
 
Self-propelled particles
 
Predict robust emergent behaviours occur in swarms inde- pendent of the type of animal that is in the swarm.
 
• Buhl, J.; Sumpter, D. J. T.; Couzin, D.; Hale, J. J.; Desp- land, E.; Miller, E. R.; Simpson, S. J. (2006). “From dis- order to order in marching locusts” (PDF). Science 312 (5778): 1402–1406.
 
  
Stochastic diffusion search
 
An agent-based probabilistic global search and optimiza- tion technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions.
 
 
A comprehensive mathematical framework.
 
A comprehensive mathematical framework.
Bishop, J.M., Stochastic Searching Networks, Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329- 331, London, UK, (1989).
+
 
Multi-swarm optimization
+
* Bishop, J.M., Stochastic Searching Networks, Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329- 331, London, UK, (1989).
Use of multiple sub-swarms instead of one (standard) swarm.
+
 
Multi-swarm system effectively combines components from Particle swarm optimization, Estimation of distri- bution algorithm, and Differential evolution into a multi- swarm hybrid.
+
''Multi-swarm optimization.'' Use of multiple sub-swarms instead of one (standard) swarm.
 +
 
 +
Multi-swarm system effectively combines components from Particle swarm optimization, Estimation of distribution algorithm, and Differential evolution into a multiswarm hybrid.
 +
 
 +
{{references}}

Latest revision as of 21:22, 4 November 2019

Intellectus (Nous)

Capacity for

  • logic
  • abstract thought
  • understanding
  • self-awareness
  • communication
  • learning
  • emotional knowledge
  • memory
  • planning
  • creativity and problem solving
Intellectus.png

Credit: E. Grant[1].

Intelligence in Nature

Animals

  • Human
  • Non-human - g Factor
    • Vertabrates: Mammals, birds, reptiles, fish Cephalopods
    • Arthropods
    • Plants - Perception?

Neuroscience and intelligence

Human

  • Brain volume
  • Grey matter
  • White matter
  • Cortical thickness
  • Neural efficiency

Primate

  • Brain size

Brain-to-body mass ratio

Nocturnal-squid.jpg

Credit: Wikimedia Commons, Nhobgood[2].

Artificial Intelligence

Practopoiesis

Conceptual bridge between biological and artificial intelligence.

  • Weak AI
  • Strong AI

AI-hard or AI-complete

Artificial agent

A LI’L HISTORY

-360 Aristotle described the syllogism, a method of formal, mechanical thought.

1206 Al-Jazari created a programmable orchestra of mechanical human beings

1600 René Descartes proposed that bodies of animals are nothing more than complex machines

1642 Blaise Pascal invented the mechanical calculator, the first digital calculating machine

1769 Wolfgang von Kempelen built and toured with his chess-playing automaton, The Turk

1913 Bertrand Russell and Alfred North Whitehead published Principia Mathematica, which revolutionized formal logic

1931 Kurt Gödel, father of theoretical computer science

1950 Alan Turing proposes the Turing Test as a measure of machine intelligence

1997 The Deep Blue chess machine (IBM) defeats the (then) world chess champion, Garry Kasparov

2005 Blue Brain is born, a project to simulate the brain at molecular detail

2011 IBM’s Watson computer defeated television game show Jeopardy! champions Rutter and Jennings

2011 Apple’s Siri, Google’s Google Now and Microsoft’s Cortana are smartphone apps that use natural language to answer questions, make recommendations and perform actions

AI Tools

Search and optimization

Search algorithm, Mathematical optimization and Evolutionary computation

Logic

Logic programming and Automated reasoning

Probabilistic methods for uncertain reasoning

Bayesian network, Hidden Markov model, Kalman filter, Decision theory and Utility theory

Classifiers and statistical learning methods

Classifier (mathematics), Statistical classification and Machine learning

Neural networks

Artificial neural network and Connectionism

Control theory Languages

42

In the radio series and the first novel, a group of hyper-intelligent pan-dimensional beings demand to learn the Answer to the Ultimate Question of Life, The Universe, and Everything from the supercomputer, Deep Thought, specially built for this purpose. It takes Deep Thought 71⁄2 million years to compute and check the answer, which turns out to be 42. Deep Thought points out that the answer seems meaningless because the beings who instructed it never actually knew what the Question was. The Ultimate Question: What do you get if you multiply six by nine? The Answer: 613 × 913 = 4213

Swarm Intelligence

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. Introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. Examples in natural systems of SI: • Ant colonies • Bird flocking • Animal herding • Bacterial growth • Fish schooling • Microbial intelligence Inspiration from Nature 1. Social Insects • Natural Navigation • Natural Clustering • Natural construction 2. Foraging 3. Flocking

Algorithms

Nature Inspired Search Techniques

Particle swarm optimization. Simulating social behaviour.

  • Kennedy, J.; Eberhart, R. (1995). “Particle Swarm Optimization”. Proceedings of IEEE International Conference on Neural Networks IV. pp. 1942–1948.
  • Shi, Y.; Eberhart, R.C. (1998). “A modified particle swarm optimizer”. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.
  • Kennedy, J. (1997). “The particle swarm: social adaptation of knowledge”. Proceedings of IEEE International Conference on Evolutionary Computation. pp. 303– 308.
Psi-example.png

Credit: M. E. H. Pedersen[3].

Ant colony optimization. A probabilistic technique in metaheuristic optimizations.

  • A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.
  • M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italy, 1992.
Aco-example.png

Credit: Wikimedia Commons[4].

Artificial bee colony algorithm. Intelligent foraging behaviour.

  • D. Dervis Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005.

Multi-level thresholding

MR brain image classification Face pose estimation

Differential evolution. A method that optimizes a problem by iteratively trying to improve a candidate solution.

  • Storn, R.; Price, K. (1997). “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces”. Journal of Global Optimization 11: 341–359..
  • Storn, R. (1996). “On the usage of differential evolution for function optimization”. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS). pp. 519–523.

Parallel computing

Multi-objective optimization

Constrained optimization

The bees algorithm. A population-based search algorithm.

  • Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
  • Pham, D.T., Castellani, M. (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
  • Pham, D.T. and Castellani, M. (2013), Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms, Soft Computing, 1-33.

Optimisation of classifiers/Clustering systems

Manufacturing

Bioengineering

Multi-objective optimization

Artificial immune systems. A class of computationally intelligent systems. Adaptive systems.

  • J.D. Farmer, N. Packard and A. Perelson, (1986) “The immune system, adaptation and machine learning”, Physica D, vol. 2, pp. 187–204.

Bioinformatics

Bat algorithm. A metaheuristic optimization algorithm.

  • X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).

Engineering design

Classifications of gene expression data

Glowworm swarm optimization. The algorithm makes the agents glow at intensities approximately proportional to the function value being optimized.

  • K.N. Krishnanand and D. Ghose (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84- 91.
  • K.N. Krishnanand and D. Ghose. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3):209- 222.
  • K.N. Krishnanand and D. Ghose. (2009). Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2):87- 124.
  • K.N. Krishnanand and D. Ghose. (2008). Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7):549- 569.

Gravitational search algorithm. Based on the law of gravity and the notion of mass interactions.

  • Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S. (2009). “GSA: a gravitational search algorithm”. Information Science 179 (13): 2232–2248.

Self-propelled particles. Predict robust emergent behaviours occur in swarms independent of the type of animal that is in the swarm.

  • Buhl, J.; Sumpter, D. J. T.; Couzin, D.; Hale, J. J.; Despland, E.; Miller, E. R.; Simpson, S. J. (2006). “From disorder to order in marching locusts” (PDF). Science 312 (5778): 1402–1406.

Stochastic diffusion search. An agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions.

A comprehensive mathematical framework.

  • Bishop, J.M., Stochastic Searching Networks, Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329- 331, London, UK, (1989).

Multi-swarm optimization. Use of multiple sub-swarms instead of one (standard) swarm.

Multi-swarm system effectively combines components from Particle swarm optimization, Estimation of distribution algorithm, and Differential evolution into a multiswarm hybrid.



References

  1. Edward Grant, "Celestial Orbs in the Latin Middle Ages", Isis, Vol. 78, No. 2. (Jun., 1987), pp. 152-173.
  2. Wikimedia Commons, Nhobgood.
  3. Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group.
  4. Wikimedia Commons.


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