A new swarm optimal collective searching behaviour framework using decision-making under risk

Al-Dulaimy, AIA 2012, A new swarm optimal collective searching behaviour framework using decision-making under risk , PhD thesis, University of Salford.

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Swarm Intelligence (SI) is a recent computational intelligence technique which mimics and makes use of the collective behaviour of flocks of birds or schools of fish for solving search and optimization problems. There are several decision-making models that have been introduced in the literature on collective searching behaviour. However, those decision models are based on the Expected Utility Theory (BUT) and tend to optimize the outcome value of the utility function; in other words, the logical decision processes used in these models are rational and risk avert and they perform poorly where risk is associated with the environment. In this research, we will use the particle swarm metaphor as a model for the human social group strategic adaptation for collective searching in a risky environment. The objective is to show that endowing these particles with a human descriptive model (irrational behaviour) from the field of psychology (using a theory named Prospect Theory (PT)) can considerably improve the global searching ability of the swarm. Unlike other proposed decision models, the BUT and other decision methods used in collective searching, this proposed searching framework captures common human decision-making attitudes towards risk, i.e., risk aversion and risk seeking, which is vital for handling the risk of violating environmental constraints, hence improving the exploration/exploitation during the evolutionary process. The experimental results presented in this research provide evidence on the robustness, the effectiveness and the practicability of the proposed framework when applied to swarm robotics and many other engineering systems with a single objective function under constraints.

Item Type: Thesis (PhD)
Contributors: Nefti-Meziani, S (Supervisor)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: Institutional Repository
Date Deposited: 27 Jul 2021 12:44
Last Modified: 04 Aug 2022 11:23
URI: http://usir.salford.ac.uk/id/eprint/61310

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