A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls
This is an incremental improvement for researchers and practitioners in optimization, offering a new meta-heuristic based on biological inspiration.
The paper tackled the problem of computationally expensive and unreliable search algorithms for optimization by proposing the Infrasonic Search Algorithm, which achieved competitive solutions on 23 benchmark test functions compared to existing methods like Genetic Algorithm and Particle Swarm Optimization.
Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.