Nature-inspired Meta-heuristic Optimization Algorithms: Grey Wolf Optimization, Dolphin Swarm Optimization, And Bacterial Foraging Optimization

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Presented at NCS2019 2020 by

Büsra Mete

Dogukan Arslan

In mathematical sense, optimization can be defined as minimizing or maximizing a function. Meta-Heuristic Optimization Algorithms aim to find the best solution -in other words the optimum solution- from the search space to the current problem as soon as possible. Today, many optimization techniques that have been developed inspired by biological systems and also their behaviour in nature are used for the solution of diverse optimization problems. One of the metaheuristic algorithms is Grey Wolf Optimization (GWO) and it is cultivated by observing the hunting strategy and the communal behaviour of grey wolf swarms. Dolphin Swarm Optimization (DSO) is another optimization algorithm and it was implemented by modelling the living habits and also the biological characteristics shown in the dolphin's real predatory course. Bacterial Foraging Optimization (BFO), the last algorithm that we have investigated, was developed with inspiration from the social foraging action of Escherichia coli. In this study, we reviewed these three nature-inspired optimization algorithms and also we applied some benchmark test functions only to GWO and presented the results.