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Fire and manoeuvrer optimizer for flow shop scheduling problems

Abstract

The purpose of this paper is to introduce a new rich source of ideas and techniques that could be used to build new algorithms capable to solve numerous encountered optimisation problems in different fields of science and engineering. The art of war is one of the most rich disciplines in terms of already experimented strategies and tactics that can inspire researchers to design new powerful and efficient metaheuristics. The framework of the proposed method are inspired by the main war phases and contains seven components: initialization, intelligence, conception, suppression, advance, assault and exploitation. The basic fire and manoeuvre tactic is adopted in the suppression and advance phases. The proposed fire and manoeuvre algorithm (FMA) is a hybridization of a greedy algorithm with a multi-neighbouring search procedure. The developed algorithm has been employed to minimize makespan of the classical flow shop scheduling problem. A mathematical model is presented to describe the studied optimisation problem. Comparative experiments on Taillard’s data set confirmed that the (FMA) results are more accurate than already published data. A comparison between the FMA and other popular nature-inspired algorithms has been conducted. It was revealed that the proposed metaheuristic outperforms the classical genetic algorithm, the migrating birds optimisation and the whale optimisation algorithm.

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Data availability

The data used the support the findings of this study are available from the corresponding author upon request.

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Belabid, J. Fire and manoeuvrer optimizer for flow shop scheduling problems. Evol. Intel. (2022). https://doi.org/10.1007/s12065-022-00767-2

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Keywords

  • Hunting algorithm
  • Flow shop
  • Mixed-integer linear programming
  • Nature inspired algorithm