Efficient Elevator Scheduling Algorithm Mimicking the Human Immune System

Steven Kwon
Chadwick International School, Incheon, Korea

Abstract

This paper introduces a novel elevator scheduling algorithm inspired by the human immune system,
particularly focusing on the Clonal Selection principle. Traditional elevator scheduling methods like
CSCAN or Genetic Algorithms (GA) have been widely used but present limitations in efficiency and
adaptability. The proposed Clonal Selection Algorithm (CSA) addresses these challenges by mimicking
the adaptive nature of the immune response to pathogens. The study first outlines the theoretical
framework of the CSA, drawing parallels between immunological processes and algorithmic strategies.
Comprehensive simulations demonstrate that the CSA significantly outperforms the GA in key aspects
such as response time, energy efficiency, and adaptability to varying traffic patterns in high-rise
buildings. This research contributes to the field of bio-inspired computing by effectively applying
biological principles to complex computational problems, offering insights into the potential of such
interdisciplinary approaches.

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