Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Nabila H Shikoun; Islam S Fathi

doi:10.61927/igmin172

60 of 132

Contribution to the Knowledge of Ground Beetles (Coleoptera: Carabidae) from Pakistan

Zubair Ahmed, Haseeb Ahmed Lalika, Imran Khatri and Eric Kirschenhofer

62 of 132

Investigation of Lateral Vibrations in Turbine-generator Unit 5 of the Inga 2 Hydroelectric Power Plant

André Mampuya Nzita, Edmond Phuku Phuati, Robert Muanda Ngimbi, Guyh Dituba Ngoma and Nathanaël Masiala Mavungu

Technology Group Research Article Article ID: igmin172

Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Data Science Machine Learning Data Mining

Nabila H Shikoun ^1,2 and

Islam S Fathi ^* ¹

Affiliation

Department of Information Systems, Al Alson Higher Institute, Cairo 11762, Egypt | Department of Systems and Computer Engineering, Faculty of Engineering, Alazhar University, Cairo, Egypt

Department of Information Systems, Al Alson Higher Institute, Cairo 11762, Egypt

DOI10.61927/igmin172 Fulltext HTML Fulltext PDF Cite this article

Abstract

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.

Figures

References

Azizi M, Aickelin U, A Khorshidi H, Baghalzadeh Shishehgarkhaneh M. Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization. Sci Rep. 2023 Jan 5;13(1):226. doi: 10.1038/s41598-022-27344-y. PMID: 36604589; PMCID: PMC9816156.
Boussaïd I, Lepagnot J, and Siarry P, A survey on optimization metaheuristics. Inform Sci. 2013; 237: 82–117.
Holland JH, Reitman JS. Cognitive systems based on adaptive algorithms. ACM SIGART Bull. 1977; 49–49.
Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life. 1991; 134–42.
Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. 1995; 39–43.
Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim. 2007; 39:459–71.
Rajabioun R. Cuckoo optimization algorithm. Appl Soft Comput. 2011; 11:5508–18.
Yang X-S. Firefly algorithm, Levy flights and global optimization. In: Research and development in intelligent systems XXVI. Springer. 2010; 209–18.
Seyedali M, Mohammad MS, Andrew L. Grey wolf optimizer. Adv Eng Software. 2014; 69:46–61.
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 2020; 191:
MiarNaeimi F, Azizyan G, Rashki M. ‘Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems.’ Knowl.-Based Syst. 2021; 213: 106711.
Trojovská E, Dehghani M, Leiva V. Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering. Biomimetics. 2023; 8: 239.
Trojovský P, Dehghani M. A new bio‑inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Scientific Reports. 2023; 13: 8775.
Elmanakhly DA. An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis. IEEE Access; 9, 2021.
Yilmaz S, Kucuksille E. A New Modification Approach on Bat Algorithm for Solving Optimization Problems. Applied Soft Computing Journal.
Chechkin A, Gonchar V, klafter J, Metzler R. Fundomentals of Lévy Flight processes. Adv Chem Phys. 2006; 133B: 439.
Fathi S, Makhlouf MAA, Osman E, Ahmed MA. An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems. IEEE Access. 2022; 10: 39129-39144.
ELmanakhly DA. BinHOA: Efficient Binary Horse Herd Optimization Method for Feature Selection: Analysis and Validations. IEEE ACCESS. 2022; 10.
Scharf I, Ovadia O. Factors influencing site abandonment and site selection in a sit-and-wait predator: a review of pit-building antlion larvae. J Insect Behav. 2006; 19:197–218.
Grzimek B, Schlager N, Olendorf D, McDade MC. Grzimek’s animal life encyclopedia. Michigan: Gale Farmington Hills. 2004.
Yang XS. Nature-Inspired Metaheuristic Algorithms second edition.
Brown CT, Liebovitch LS, Glendon R, Lévy Flights in Dobe Ju/’hoansi Foraging Patterns. Hum Ecol. 2007; 35:129-138.
Cuevas E, Echavarría A, Ramírez-Ortegón MA. An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Applied Intelligence. 2014; 40(2): 256-272.
Yang XS. A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer; 2010; 65–74.
Yang XS. Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. 2012; 240–9.
Yang XS, Deb S. Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing. 2009. NaBIC 2009; 210–4.

Similar Articles

The Relationship between Energy and Climate Warming
Kamalov Bakhodir Asamovich
DOI10.61927/igmin194

Potentially Toxic Metals in Cucumber Cucumis sativus Collected from Peninsular Malaysia: A Human Health Risk Assessment
Chee Kong Yap, Rosimah Nulit, Aziran Yaacob, Zaieka Shamsudin, Meng Chuan Ong, Wan Mohd Syazwan, Hideo Okamura, Yoshifumi Horie, Chee Seng Leow, Ahmad Dwi Setyawan, Krishnan Kumar, Wan Hee Cheng and Kennedy Aaron Aguol
DOI10.61927/igmin200

The Power of Artificial Intelligence for Improved Patient Outcomes, Ethical Practices and Overcoming Challenges
Imad-Addin Almasri
DOI10.61927/igmin222

Revolutionizing Duplicate Question Detection: A Deep Learning Approach for Stack Overflow
Muhammad Faseeh and Harun Jamil
DOI10.61927/igmin135

Auxological Status of Modern Primary School Students of Nizhny Novgorod Region
Kalyuzhny Evgeniy Aleksandrovich, Mukhina Irina Vasilievna, Bogomolova Elena Sergeevna, Galova Elena Anatolyevna, Puzhak Svetlana Andreevna and Baklanova Ekaterina Sergeevna
DOI10.61927/igmin219

Indigenous Environmental Resilience: Decoding Ancient Rozvi Wisdom on Mountain Ecosystems as Disaster Management Solutions
Lesley Hatipone Machiridza
DOI10.61927/igmin130

DNA Genetics and UHPLC-Q-TOF-MS Analysis of Phytochemicals for Asparagus racemosus Roots
Nguyen Thi Huong, Do Ngoc Thuy, Phung Van Trung, Le Ngoc Hung and Mai Van Nam
DOI10.61927/igmin217

Compatibility Analysis of Frequency Containment Reserve and Load Frequency Control Functions
Ramūnas Deltuva
DOI10.61927/igmin237

New Scientific Field for Modelling Complex Dynamical Systems: The Cybernetics Artificial Intelligence (CAI)
Peter P Groumpos
DOI10.61927/igmin183

The Kazakh Language Requires Reform of its Writing
Sharipbay Altynbek
DOI10.61927/igmin148

研究を公開する

私たちは、科学、技術、工学、医学に関する幅広い種類の記事を編集上の偏見なく公開しています。

提出する

見る原稿のガイドライン追加論文処理料

ソーシャルアイコン

主題別に参照する

[1] Azizi M, Aickelin U, A Khorshidi H, Baghalzadeh Shishehgarkhaneh M. Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization. Sci Rep. 2023 Jan 5;13(1):226. doi: 10.1038/s41598-022-27344-y. PMID: 36604589; PMCID: PMC9816156.

[2] Boussaïd I, Lepagnot J, and Siarry P, A survey on optimization metaheuristics. Inform Sci. 2013; 237: 82–117.

[3] Holland JH, Reitman JS. Cognitive systems based on adaptive algorithms. ACM SIGART Bull. 1977; 49–49.

[4] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life. 1991; 134–42.

[5] Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. 1995; 39–43.

[6] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim. 2007; 39:459–71.

[7] Rajabioun R. Cuckoo optimization algorithm. Appl Soft Comput. 2011; 11:5508–18.

[8] Yang X-S. Firefly algorithm, Levy flights and global optimization. In: Research and development in intelligent systems XXVI. Springer. 2010; 209–18.

[9] Seyedali M, Mohammad MS, Andrew L. Grey wolf optimizer. Adv Eng Software. 2014; 69:46–61.

[10] Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 2020; 191:

[11] MiarNaeimi F, Azizyan G, Rashki M. ‘Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems.’ Knowl.-Based Syst. 2021; 213: 106711.

[12] Trojovská E, Dehghani M, Leiva V. Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering. Biomimetics. 2023; 8: 239.

[13] Trojovský P, Dehghani M. A new bio‑inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Scientific Reports. 2023; 13: 8775.

[14] Elmanakhly DA. An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis. IEEE Access; 9, 2021.

[15] Yilmaz S, Kucuksille E. A New Modification Approach on Bat Algorithm for Solving Optimization Problems. Applied Soft Computing Journal.

[16] Chechkin A, Gonchar V, klafter J, Metzler R. Fundomentals of Lévy Flight processes. Adv Chem Phys. 2006; 133B: 439.

[17] Fathi S, Makhlouf MAA, Osman E, Ahmed MA. An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems. IEEE Access. 2022; 10: 39129-39144.

[18] ELmanakhly DA. BinHOA: Efficient Binary Horse Herd Optimization Method for Feature Selection: Analysis and Validations. IEEE ACCESS. 2022; 10.

[19] Scharf I, Ovadia O. Factors influencing site abandonment and site selection in a sit-and-wait predator: a review of pit-building antlion larvae. J Insect Behav. 2006; 19:197–218.

[20] Grzimek B, Schlager N, Olendorf D, McDade MC. Grzimek’s animal life encyclopedia. Michigan: Gale Farmington Hills. 2004.

[21] Yang XS. Nature-Inspired Metaheuristic Algorithms second edition.

[22] Brown CT, Liebovitch LS, Glendon R, Lévy Flights in Dobe Ju/’hoansi Foraging Patterns. Hum Ecol. 2007; 35:129-138.

[23] Cuevas E, Echavarría A, Ramírez-Ortegón MA. An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Applied Intelligence. 2014; 40(2): 256-272.

[24] Yang XS. A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer; 2010; 65–74.

[25] Yang XS. Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. 2012; 240–9.

[26] Yang XS, Deb S. Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing. 2009. NaBIC 2009; 210–4.

Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Affiliation

Abstract

Figures

References

Similar Articles

研究を公開する

ソーシャルアイコン

Page Navigation

主題別に参照する

私たちを識別する

今すぐ公開する

その他のサービス

政策

電子書籍

原稿のガイドライン

連絡