Abstract
The performance of most data science algorithms, and in particular machine learning algorithms, is largely dependent on the performance of their optimisation algorithm. In other words, without an effective optimisation algorithm there is no effective data science algorithm. Conventional optimisation algorithms suffer from drawbacks such as a tendency to get stuck in local optima and sensitivity to the initial conditions. To tackle these, population-based metaheuristic algorithms, which work on a population of candidate solution and incorporate stochastic behaviour, can be used. In this chapter, we the present the Human Mental Search (HMS) algorithm, population-based metaheuristic algorithm that is inspired by bid exploration in online auctions. HMS comprises three main operators:, mental search, grouping, and movement. The mental search operator is responsible for exploring the vicinity of a candidate solution, the grouping operator employs an unsupervised clustering technique, k-means, to partition candidate solutions, while the movement operator moves candidate solutions towards a promising area identified by the grouping operator. To evaluate the efficacy of the HMS algorithm, a set of experiments on different benchmark functions with diverse characteristics as well as both normal and large-scale problems. The obtained results clearly show the merit of HMS compared to other algorithms.
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Mousavirad, S.J., Schaefer, G., Ebrahimpour-Komleh, H. (2021). The Human Mental Search Algorithm for Solving Optimisation Problems. In: Hassanien, AE., Taha, M.H.N., Khalifa, N.E.M. (eds) Enabling AI Applications in Data Science. Studies in Computational Intelligence, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-52067-0_2
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