A Monarch Butterfly Optimization Approach to Dynamic Task Scheduling

  • Chouhan Kumar RathEmail author
  • Prasanti Biswal
  • Shashank Sekhar Suar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1089)


Parallel processing has been employed many years in high-performance computing. Parallel processing is mostly used because it provides concurrency, maximizes load balancing, and minimizes system idle time and execution time which depend on various computer architecture. Task scheduling is a parallel processing technique which allocates the tasks to various processors. Mapping of heterogeneous tasks to a heterogeneous processor or core dynamically in a distributed environment is one of the active area of research in the field of parallel computing system. Here in this paper, monarch butterfly optimization (MBO) [1], a nature-inspired metaheuristic algorithm is implemented for a task scheduling problem. A monarch, a North American butterfly is best known for its migratory behavior in the summer season. There are mainly two processes to get the best solution. First, using a migration operator, a new generation is created. Second, they update their position by using a butterfly adjusting operator. The fitness value is evaluated and updated the population with a higher fitness value to satisfy the objective of the problem. Minimizing the cost and time of the scheduling strategy is the main objective of the proposed work.


Task scheduling Parallel processing Monarch Butterfly Optimization (MBO) Genetic Algorithm (GA) Evolutionary computation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Chouhan Kumar Rath
    • 1
    Email author
  • Prasanti Biswal
    • 1
  • Shashank Sekhar Suar
    • 1
  1. 1.Sambalpur University Institute of Information and TechnologySambalpurIndia

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