An Efficient and Improved Coronavirus Herd Immunity Algorithm Using Knowledge-Driven Variable Neighborhood Search for Flexible Job-Shop Scheduling Problems

Abstract

By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized two-stage individual encoding and decoding scheme. Secondly, in order to realize the multi-scale search of the solution space, a multi-population update mechanism is designed, and a collaborative learning method is proposed to ensure the diversity of the population. Then, an adaptive mutation operation is introduced to enrich the diversity of the population, relying on the adaptive adjustment of the mutation operator to balance global search and local search capabilities. In order to realize a directional and efficient neighborhood search, this algorithm proposed a knowledge-driven variable neighborhood search strategy. Finally, the algorithm’s performance comparison experiment is carried out. The minimum makespans on the MK06 medium-scale case and MK10 large-scale case are 58 and 201, respectively. The experimental results verify the effectiveness of the hybrid algorithm.

A State Transition Diagram and an Artificial Physarum polycephalum Colony Algorithm for the Flexible Job Shop Scheduling Problem with Transportation Constraints

“…It is very important and challenging to design efficient algorithms to address it in large-sized cases, such as simulated annealing (SA) [6] and fuzzy logic (FL) [11]. Among them, swarm intelligence (SI) algorithms have received great attention [23,26], i.e., genetic algorithms (GAs) [2,4,16], particle swarm optimiza-tion (PSO) [6,19], ant colony optimization (ACO) [8], deep learning (DL), artificial neural networks (ANNs) [12,27], artificial bee colony (ABC) [13], adaptive memetic algorithms (AMAs) [14], migrating birds optimization [17], grey wolf optimization (GWO) [20], quantum cat swarm optimization [22], artificial slime mold [28], artificial Physarum swarm [29], coronavirus herd immunity [30], artificial plant community [31,32], whale optimization [33], artificial algae [34], and the Jaya algorithm [35]. However, these swarm intelligence algorithms are also prone to fall into local optimization prematurely, and some scholars have tried to improve algorithm performance using hybrid algorithms [6,36].…”

“…The "Ref" column in Table 2 gives the benchmark values from [37], which are weak lower-bound values obtained only with constraint propagation. The solving errors were calculated as in Equation (30). As can be seen from the results in Table 2, the APPC can outperform most algorithms with better solutions and a shorter computation time.…”