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A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems

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Abstract

This paper presents a novel approach based on the combination of the Modified Brain Storm Optimization algorithm (MBSO) with a simplified version of the Constraint Consensus method as special operator to solve constrained numerical optimization problems. Regarding the special operator, which aims to reach the feasible region of the search space, the consensus vector becomes the feasibility vector computed by the hardest constraint in turn for a current infeasible solution; then the operations to mix the other feasibility vectors are avoided. This new combined algorithm, named as MBSO-R+V, solves a suit of eighteen test problems in ten and thirty dimensions. From a set of experiments related to the location and frequency of application of the constraint consensus method within MBSO, a suitable design of the combined approach is presented. This proposal shows encouraging final results while being compared against state-of-the-art algorithms, showing that it is viable to add special operators to improve the capabilities of swarm-intelligence algorithms when dealing with continuous constrained search spaces.

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Acknowledgments

The first author acknowledges support from the Mexican Council of Science and Technology (CONACyT) and the University of Veracruz to pursue graduate studies at its Artificial Intelligence Research Center. The second author acknowledges support from CONACyT through project No. 220522.

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Authors and Affiliations

  1. Artificial Intelligence Research Center, University of Veracruz, Sebastian Camacho 5, Xalapa, Veracruz, 91000/Centro, México

    Adriana Cervantes-Castillo & Efrén Mezura-Montes

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  1. Adriana Cervantes-Castillo
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  2. Efrén Mezura-Montes
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Correspondence to Adriana Cervantes-Castillo.

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Cervantes-Castillo, A., Mezura-Montes, E. A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems. Appl Intell 50, 4145–4161 (2020). https://doi.org/10.1007/s10489-020-01763-8

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