Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem

Ying Sun, Jeng-Shyang Pan, Pei Hu and Shu-Chuan Chu
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Ying Sun: Shandong University of Science and Technology
Jeng-Shyang Pan: Shandong University of Science and Technology
Pei Hu: Shandong University of Science and Technology
Shu-Chuan Chu: Shandong University of Science and Technology

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 4, No 7, 1639-1665

Abstract: Abstract The Equilibrium Optimizer (EO) algorithm is a new meta-heuristic algorithm that uses an equilibrium pool and candidates to update particles (solutions). EO algorithm not only has strong exploitation and exploration capabilities but also avoids falling into the local optimum. The reason why EO has these advantages is because of the existence of “generation rate”. This paper proposes an Enhanced Equilibrium Optimizer (EEO) Algorithm based on three communication strategies to solve the Job Shop Scheduling Problem (JSSP). To prove the accuracy of the algorithm, this paper uses 28 benchmark functions for testing. At the same time, the Enhanced Equilibrium Optimizer (EEO1, EEO2, EEO3) Algorithms are compared with the existing optimization methods, including Grey Wolf Optimizer (GWO), Multi-Version Optimizer (MVO), Differential Evolution (DE), Whale Optimization Algorithm (WOA). Experiments show that the EO algorithm is significantly better than GWO, MVO, DE, WOA. EO algorithm is mainly used to optimize continuous problems, but JSSP is a discrete application, so the standard equilibrium optimizer algorithm needs to be discretized. This paper extends the enhanced equilibrium optimizer algorithm and adds discretization processing to JSSP. The algorithm is also applied for the job shop scheduling problem by discretization and is compared with the three improvement methods of EEO. Experimental results prove that the algorithm has made significant improvements in solving JSSP.

Keywords: Equilibrium Optimizer; Discrete; Job shop scheduling problem; Meta-heuristic algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01899-5

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