Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (5): 2465-2485.doi: 10.1007/s42235-023-00356-8

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The Bedbug Meta‑heuristic Algorithm to Solve Optimization Problems

Kouroush Rezvani1; Ali Gafari2; Mohammad Reza Ebrahimi Dishabi1   

  1. 1 Department of Computer Engineering, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran  2 Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
  • 出版日期:2023-08-26 发布日期:2023-09-06
  • 通讯作者: Ali Gafari E-mail:A.ghafari@iaut.ac.ir
  • 作者简介:Kouroush Rezvani1; Ali Gafari2; Mohammad Reza Ebrahimi Dishabi1

The Bedbug Meta‑heuristic Algorithm to Solve Optimization Problems

Kouroush Rezvani1; Ali Gafari2; Mohammad Reza Ebrahimi Dishabi1   

  1. 1 Department of Computer Engineering, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran  2 Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
  • Online:2023-08-26 Published:2023-09-06
  • Contact: Ali Gafari E-mail:A.ghafari@iaut.ac.ir
  • About author:Kouroush Rezvani1; Ali Gafari2; Mohammad Reza Ebrahimi Dishabi1

摘要: Small parasitic Hemipteran insects known as bedbugs (Cimicidae) feed on warm-blooded mammal’s blood. The most famous member of this family is the Cimex lectularius or common bedbug. The current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic Algorithm (BMHA). The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in nature. The two main stages of optimization algorithms, exploration, and exploitation, are designed by modeling bedbug social interaction to search for food. The proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including CEC2019. The results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization algorithms. The results also prove the new algorithm's performance in solving real optimization problems in unknown search spaces. 

关键词: Bedbug Meta-Heuristic Algorithm , · Optimization algorithm , · BMHA

Abstract: Small parasitic Hemipteran insects known as bedbugs (Cimicidae) feed on warm-blooded mammal’s blood. The most famous member of this family is the Cimex lectularius or common bedbug. The current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic Algorithm (BMHA). The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in nature. The two main stages of optimization algorithms, exploration, and exploitation, are designed by modeling bedbug social interaction to search for food. The proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including CEC2019. The results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization algorithms. The results also prove the new algorithm's performance in solving real optimization problems in unknown search spaces. 

Key words: Bedbug Meta-Heuristic Algorithm , · Optimization algorithm , · BMHA