Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

Abstract

Purpose

This paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor.

Design/methodology/approach

In the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO.

Findings

To validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass.

Originality/value

The results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors.

Citation

Bensoltane, H. and Belli, Z. (2023), "Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 42 No. 6, pp. 1905-1922. https://doi.org/10.1108/COMPEL-02-2023-0058