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Improving Whale Optimization Algorithm with Elite Strategy and Its Application to Engineering-Design and Cloud Task Scheduling Problems

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Abstract

The whale optimization algorithm (WOA), a biologically inspired optimization technique, is known for its straightforward design and effectiveness. Despite many advantages, it has certain disadvantages, such as a limited exploration capacity and early convergence as a result of the minimal exploration of the search process. The WOA cannot bypass the local solution; consequently, the search is unbalanced. This study introduces a new variant of WOA, namely elite-based WOA (EBWOA), to address the inherent shortcomings of traditional WOA. Unlike the three phases used in the traditional WOA, only the encircling prey and bubble-net attack phases are applied in the new variant. Using the local elite method, exploration will be conducted with an encircling prey phase to ensure some exploitation during exploration. The choice between exploration and exploitation is achieved by introducing a new choice parameter. An inertia weight \({(\omega }_{i})\) is used in both phases to scour the region. The EBWOA is used to evaluate twenty-five benchmark functions, IEEE CEC 2019 functions, and two design problems and compared to several fundamental techniques and WOA variants. In addition, the EBWOA is used to solve the practical cloud scheduling problem. Performance is compared against a variety of metaheuristics using real cloud workloads by running experiments on the standard CloudSim simulator. Comparing the numerical results of benchmark functions, IEEE CEC 2019 functions, statistical verification, and the solution generation speed of EBWOA confirmed the effectiveness of the proposed EBWOA approach. It has also shown a great improvement over baseline algorithms in creating efficient scheduling solutions by significantly reducing makespan time and energy consumption targets.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura, India

    Sanjoy Chakraborty

  2. Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura, India

    Sanjoy Chakraborty

  3. Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura, 799046, India

    Apu Kumar Saha

  4. Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, 143005, India

    Amit Chhabra

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  1. Sanjoy Chakraborty
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  2. Apu Kumar Saha
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Correspondence to Apu Kumar Saha or Amit Chhabra.

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Table 21 Variable and fixed dimension unimodal and multimodal functions
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Chakraborty, S., Saha, A.K. & Chhabra, A. Improving Whale Optimization Algorithm with Elite Strategy and Its Application to Engineering-Design and Cloud Task Scheduling Problems. Cogn Comput 15, 1497–1525 (2023). https://doi.org/10.1007/s12559-022-10099-z

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