Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems

Syed Kumayl Raza Moosavi; Muhammad Hamza Zafar; Seyedali Mirjalili; Filippo Sanfilippo

doi:10.1007/978-3-031-42505-9_36

Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems

Syed Kumayl Raza Moosavi
, Muhammad Hamza Zafar
, Seyedali Mirjalili
, Filippo Sanfilippo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings
Editors	Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	427-438
Number of pages	12
ISBN (Print)	9783031425042
DOIs	https://doi.org/10.1007/978-3-031-42505-9_36
Publication status	Published - 2023
Event	22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023 - Zakopane, Poland Duration: 18 Jun 2023 → 22 Jun 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14125 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023
Country/Territory	Poland
City	Zakopane
Period	18/06/23 → 22/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

Artificial Intelligence
Engineering Design Problems
Improved Barnacles Mating Optimization
Metaheuristic Algorithms

Access to Document

10.1007/978-3-031-42505-9_36

Cite this

Raza Moosavi, S. K., Zafar, M. H., Mirjalili, S., & Sanfilippo, F. (2023). Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings (pp. 427-438). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14125 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-42505-9_36

Raza Moosavi, Syed Kumayl ; Zafar, Muhammad Hamza ; Mirjalili, Seyedali et al. / Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings. editor / Leszek Rutkowski ; Rafał Scherer ; Marcin Korytkowski ; Witold Pedrycz ; Ryszard Tadeusiewicz ; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2023. pp. 427-438 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems",

abstract = "A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.",

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note = "Funding Information: This work was supported by Top Research Centre Mechatronics (TRCM), University of Agder (UiA), Norway. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023 ; Conference date: 18-06-2023 Through 22-06-2023",

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Raza Moosavi, SK, Zafar, MH, Mirjalili, S & Sanfilippo, F 2023, Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. in L Rutkowski, R Scherer, M Korytkowski, W Pedrycz, R Tadeusiewicz & JM Zurada (eds), Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14125 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 427-438, 22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023, Zakopane, Poland, 18/06/23. https://doi.org/10.1007/978-3-031-42505-9_36

Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. / Raza Moosavi, Syed Kumayl; Zafar, Muhammad Hamza; Mirjalili, Seyedali et al.
Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings. ed. / Leszek Rutkowski; Rafał Scherer; Marcin Korytkowski; Witold Pedrycz; Ryszard Tadeusiewicz; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2023. p. 427-438 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14125 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems

AU - Raza Moosavi, Syed Kumayl

AU - Zafar, Muhammad Hamza

AU - Mirjalili, Seyedali

AU - Sanfilippo, Filippo

N1 - Funding Information: This work was supported by Top Research Centre Mechatronics (TRCM), University of Agder (UiA), Norway. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.

AB - A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.

KW - Artificial Intelligence

KW - Engineering Design Problems

KW - Improved Barnacles Mating Optimization

KW - Metaheuristic Algorithms

UR - https://www.scopus.com/pages/publications/85172418811

U2 - 10.1007/978-3-031-42505-9_36

DO - 10.1007/978-3-031-42505-9_36

M3 - Conference contribution

AN - SCOPUS:85172418811

SN - 9783031425042

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 427

EP - 438

BT - Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings

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A2 - Scherer, Rafał

A2 - Korytkowski, Marcin

A2 - Pedrycz, Witold

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Y2 - 18 June 2023 through 22 June 2023

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Raza Moosavi SK, Zafar MH, Mirjalili S, Sanfilippo F. Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. In Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM, editors, Artificial Intelligence and Soft Computing - 22nd International Conference, ICAISC 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 427-438. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-42505-9_36