Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling

Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling

Kawal Jeet (D. A. V. College, India)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-5832-3.ch007

Abstract

Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithms are very efficient in identifying optimal solutions, but as the size of the problem increases, these algorithms tend to get stuck at local optima. In order to extract these algorithms from local optima, genetic algorithm has been used. Flower pollination algorithm, when appended with GA, is observed to perform better than other counterpart nature-inspired algorithms as well as existing heuristics and meta-heuristics based on MOGA and NSGA-II algorithms.
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Research Objectives

The main objectives of the proposed work are listed below:

  • To experimentally evaluate the behaviour of recent-inspired algorithms such as ABC, Bat, BH, CS, FPA and GWO algorithms on scheduling of n jobs on m potential parallel machines.

  • To evaluate the impact of adding crossover and mutation operator to these nature-inspired algorithms.

  • To investigate use of auxiliary archive to maintain Pareto Front and hypercubes to maintain best solutions.

  • Comparison of job schedules obtained by applying these hybrid nature-inspired algorithms to that of existing search-based approaches by taking summation of maximum tardiness and weighted flow time as comparison criteria.

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