A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem

Caldeira, Rylan H.; Gnanavelbabu, A.

doi:10.1016/j.eswa.2021.114567

Cited by 58 publications

(14 citation statements)

References 45 publications

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“…Different Petri-net based heuristic scheduling methods were used to obtain optimal or near-optimal schedules for FJSP [ 9 , 10 , 11 , 12 , 13 ]. The FJSP has been solved by Caldeira et al [ 14 ] using a discrete multi-objective Jaya algorithm, taking the makespan, total machine workload, and workload of essential machines as performance metrics. There have been many studies on FJSP that consider machine constraints, and many excellent results have been achieved.…”

Section: Introductionmentioning

confidence: 99%

An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems

Luan

2022

Sensors

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According to the characteristics of flexible job shop scheduling problems, a dual-resource constrained flexible job shop scheduling problem (DRCFJSP) model with machine and worker constraints is constructed such that the makespan and total delay are minimized. An improved African vulture optimization algorithm (IAVOA) is developed to solve the presented problem. A three-segment representation is proposed to code the problem, including the operation sequence, machine allocation, and worker selection. In addition, the African vulture optimization algorithm (AVOA) is improved in three aspects: First, in order to enhance the quality of the initial population, three types of rules are employed in population initialization. Second, a memory bank is constructed to retain the optimal individuals in each iteration to increase the calculation precision. Finally, a neighborhood search operation is designed for individuals with certain conditions such that the makespan and total delay are further optimized. The simulation results indicate that the qualities of the solutions obtained by the developed approach are superior to those of the existing approaches.

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Section: Introductionmentioning

confidence: 99%

An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems

Luan

2022

Sensors

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“…Each individual in the group can search for the best search agent globally and then solve complex problems through their own experience and mutual communication. It has been widely used in engineering optimization problems such as path planning [2], job shop scheduling [3], and image segmentation [4].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning

Chen

2022

J. Phys.: Conf. Ser.

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Seagull Optimization Algorithm (SOA) is an emerging intelligent optimization algorithm proposed in recent years. This paper proposes an improvement in the SOA based on Levy flight(LSOA), which aims to solve the problems of decreasing exploration ability and easing to fall into local extreme values in the late stage of SOA. LSOA could increase the diversity of the seagull population, improve the exploration ability of SOA. Then selecting the classic 23 benchmark functions for simulation experiments and comparing with the other five intelligent algorithms in recent years. The result shows that LSOA has improved stability, convergence speed, and search accuracy, effectively avoiding local optimum conditions, with broad research prospects. Finally, LSOA is applied to the path planning problem, proving the algorithm’s feasibility and having a good optimization effect.

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“…Since its introduction, Jaya has been widely used and shown remarkable performance in solving many challenging engineering problems. For instance, Jaya has been used very recently in Reference 20 for the parameter estimation of photovoltaic cell/modules, in Reference 21 for the job‐shop scheduling problem, in Reference 22 for the flexible job shop scheduling problem, and in Reference 23 for the feature selection.…”

Section: Introductionmentioning

confidence: 99%

Binary Jaya‐based channel allocation in cognitive radio networks

Saoucha

2022

Concurrency and Computation

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Summary Cognitive radio is a key solution for wireless networks, as it offers the possibility to cope with spectrum scarcity problem. In this work, we are interested in the channel allocation problem (CA) in cognitive radio networks which is a well‐known NP‐hard optimization problem. CA has been resolved using classic evolutionary approaches such as genetic algorithms (GAs) and particle swarm optimization (PSO). However, the performance of these latters depends strongly on the parameters tuning which is a hard time‐consuming task. Under this umbrella, in this article, we propose a new solution based on Jaya algorithm which is a recent free parameters approach. The performance of our proposed algorithm is compared against GA and PSO under different network topologies, and this in terms of max‐sum‐reward, max‐min‐reward, and max‐proportional‐fair. The obtained simulation results confirm the superiority of our proposed approach under all the tested scenarios.

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A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem

Cited by 58 publications

References 45 publications

An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems

An Improved African Vulture Optimization Algorithm for Dual-Resource Constrained Multi-Objective Flexible Job Shop Scheduling Problems

Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning

Binary Jaya‐based channel allocation in cognitive radio networks

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