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Artificial Cell Swarm Optimization

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Frontier Applications of Nature Inspired Computation

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Traditional meta-heuristic optimization algorithms, such as the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and bat algorithm (BA) played a vital role to provide impressive near to the optimum solutions for linear/nonlinear complex problems in numerous applications. Nevertheless, in some case, such algorithms may suffer from becoming trapped in local optima with long computational time for convergence. Thus, in order to enhance a broader view over the optimization domain, still further refined studies are carried out to develop these algorithms and to explore new ones based on the inspiration from nature. Thus, a novel meta-heuristic optimization algorithm has been proposed in the present work by employing the concept of artificial cells, which are inspired by biological living cells. An efficient application of artificial cell division (ACD) algorithm has been employed to traverse the search space while decreasing the search time. The inherent properties of ACD algorithm prevent it from premature convergence to local optima. The current work designed a novel artificial cell swarm optimization (ACSO) algorithm. The results compared the proposed algorithm performance to GA, PSO, and the bat algorithm by using seven known benchmark functions. The results established that the performance of proposed ACSO algorithm in terms of the number of iterations required to reach the expected accuracy outperformed the GA, PSO, and the Bat Algorithms. The ACSO achieved the fastest convergence with the benchmark functions with accuracies range 100 or 99% compared to the other optimization algorithms in the current study.

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Author information

Authors and Affiliations

  1. Department of CSE, University of Engineering and Management, Kolkata, West Bengal, India

    Sankhadeep Chatterjee

  2. Oracle, Bangalore, Karnataka, India

    Subham Dawn

  3. Department of CSE, Hooghly Engineering & Technology College, Hooghly, West Bengal, India

    Sirshendu Hore

Authors
  1. Sankhadeep Chatterjee
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  2. Subham Dawn
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  3. Sirshendu Hore
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Corresponding author

Correspondence to Sirshendu Hore .

Editor information

Editors and Affiliations

  1. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

  2. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta

  3. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Nilesh Patel

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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Chatterjee, S., Dawn, S., Hore, S. (2020). Artificial Cell Swarm Optimization. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_9

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