Sperm motility algorithm: a novel metaheuristic approach for global optimisation

This paper proposes a new metaheuristic approach, namely, sperm motility algorithm (SMA), inspired by the fertilisation process in humans. Sperms are randomly diffused inside the female vagina to start searching for ovum. Investigation considering the modelling process of the sperm flow typical movement is carried out leading to selection of Stokes equations as mathematical model. A heuristic mechanism of sperms guided by chemoattractant secreted by ovum is to guarantee the progressing towards the goal. When the chemoattractant concentration increases the sperms are more likely to approach the ovum. Through the mimicking of the whole fertilisation process, a search approach to find a global optimisation algorithm is achieved. The proposed algorithm is tested using several standard benchmark functions as well as two engineering problems. A comparative study of the results with those obtained using well-known swarm intelligence algorithms is to validate and verify the efficiency of SMA. Getting the benefit of fertilisation chemoattractant, the proposed algorithm managed to solve unbounded constraint optimisation problems. A global optimal solution was reached in the solution of all benchmark problems proving the capability of the new algorithm to escape from local optimum.