Information propagation in a social network: The case of a fish schooling algorithm

Abstract

The propagation of information about the environment amongst animals via social communication has attracted increasing research interest in recent decades with the realisation that many animal species engage in subtle forms of information transfer which had previously escaped notice. From an evolutionary perspective, the widespread existence of social communication mechanisms is not surprising given the significant benefits which can accrue to behaviours such as sharing of information on resources and on environmental threats. More generally, we can consider this process as information flowing between a network of nodes or agents, wherein each agent receives inputs from their senses, processes this information, and in turn through their resulting actions, can influence subsequent actions of other agents. Social communication mechanisms of organisms have inspired the development of several powerful families of optimization algorithms including ant colony optimization and honey bee optimization algorithms. One interesting example of information propagation is provided by the shoaling and schooling behaviours of fish. In this chapter we develop an optimization algorithm (the Fish Algorithm) which is inspired by the schooling behaviour of ‘golden shiner’ fish (Notemigonus crysoleucas) and explore the relative importance of social information propagation and individual perception mechanisms in explaining the resulting performance of the algorithm.