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Bee-inspired metaheuristics for global optimization: a performance comparison

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Abstract

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve continuous optimization problems. They are the bee system, mating bee optimization (MBO), bee colony optimization, bee evolution for genetic algorithms (BEGA), bee algorithm, artificial bee colony (ABC), and bee swarm optimization. The algorithms’ performances are evaluated with several benchmark problems. This study’s results rank the cited algorithms according to their convergence efficiency. The strengths and shortcomings of each algorithm are discussed. The ABC, BEGA, and MBO are the most efficient algorithms. This study’s results show the convergence rate among different algorithms varies, and evaluates the causes of such variation.

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Code availability

The codes are available at https://github.com/rmsolgi/bee-inspired-metaheuristics.

Notes

  1. Fitness function refers to a penalized objective function, that is, the objective function with constraints added to it as penalty.

  2. In this manuscript selections always are done with replacement.

  3. This work applies uniform crossover in all algorithms in which the crossover function is used. The number and place of crossover points are random and uniformly distributed (Bozorg-Haddad et al. 2017a, b).

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  1. University of California Santa Barbara (UCSB), Santa Barbara, CA, USA

    Ryan Solgi

  2. Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA, USA

    Hugo A. Loáiciga

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  1. Ryan Solgi
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Solgi, R., Loáiciga, H.A. Bee-inspired metaheuristics for global optimization: a performance comparison. Artif Intell Rev 54, 4967–4996 (2021). https://doi.org/10.1007/s10462-021-10015-1

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