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Artificial Cell Swarm Optimization

  • Sankhadeep Chatterjee
  • Subham Dawn
  • Sirshendu HoreEmail author
Chapter
  • 21 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (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.

Keywords

Artificial cell division Genetic algorithm Meta-heuristic Particle swarm optimization algorithm Bat algorithm Benchmark functions Single objective optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sankhadeep Chatterjee
    • 1
  • Subham Dawn
    • 2
  • Sirshendu Hore
    • 3
    Email author
  1. 1.Department of CSEUniversity of Engineering and ManagementKolkataIndia
  2. 2.OracleBangaloreIndia
  3. 3.Department of CSEHooghly Engineering & Technology CollegeHooghlyIndia

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