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

Swarm Intelligence (SI) is a recent computational intelligence technique which mimicsand makes use of the collective behaviour of flocks of birds or schools of fish forsolving search and optimization problems. There are several decision-making modelsthat have been introduced in the literature on collective searching behaviour. However,those decision models are based on the Expected Utility Theory (BUT) and tend tooptimize the outcome value of the utility function; in other words, the logical decisionprocesses used in these models are rational and risk avert and they perform poorlywhere risk is associated with the environment.In this research, we will use the particle swarm metaphor as a model for the humansocial group strategic adaptation for collective searching in a risky environment. Theobjective is to show that endowing these particles with a human descriptive model(irrational behaviour) from the field of psychology (using a theory named ProspectTheory (PT)) can considerably improve the global searching ability of the swarm.Unlike other proposed decision models, the BUT and other decision methods used incollective searching, this proposed searching framework captures common humandecision-making attitudes towards risk, i.e., risk aversion and risk seeking, which isvital for handling the risk of violating environmental constraints, hence improving theexploration/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 toswarm robotics and many other engineering systems with a single objective functionunder constraints