DEVELOPMENT OF A SMELL AGENT OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS

📄 Project Abstract

This thesis presents the development of smell agent optimization (SAO) algorithm. The developed algorithm consists of three modes (sniffing, trailing and random modes). The evaporation of smell molecules from the smell source is modelled into sniffing mode using the concept of the hydrostatic pressure of gas and positions of molecules. The fitness of the sniffing mode is evaluated and the molecule with the most favourable fitness is taken as the agent. The olfaction capacity of the agent is then evaluated and the training mode is developed using the current position of the agent and the position of the molecules with the current worst fitness. In practical scenarios, it is usually difficult for the agent to account for all the evaporating smell molecules due to the Brownian nature of the smell molecules. This is largely responsible for the agent to getting trapped in a "state of confusion" and consequently leading to the loss of smell trail. To account for this situation in the SAO, a ...