A Discrete Krill Herd Method with Multilayer Coding Strategy for Flexible Job-Shop Scheduling Problem

Wang, Gai-Ge; Deb, Suash; Thampi, Sabu M.

doi:10.1007/978-3-319-23036-8_18

Cited by 11 publications

(5 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[24]. To satisfy different objective and application, KH algorithm has been extended in recent years, such as chaotic-based KH algorithm [31], fuzzy-based KH algorithm [32], discrete-based KH algorithm [33] and so on. For its noticeable advantages in term of simplicity, flexibility, computationally efficiency, KH algorithm has been extensively used in many fields (e.g.…”

Section: Related Workmentioning

confidence: 99%

Krill Herd Algorithm for Signal Optimization of Cooperative Control With Traffic Supply and Demand

Cao

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

As a novel signal control method, signal Cooperative Control with traffic Supply and Demand (CCSD) is superior to the traditional control methods and could satisfy the control requirements under all traffic conditions. However, the optimization solution of CCSD cannot meet the real-time control requirement for its exhaustive search. To overcome the problem, in this paper, the optimization problem of CCSD is reconstructed by the time-varying traffic supply and demand, and the problem is reduced as a problem of space search. Then, the krill herd (KH) algorithm is introduced and employed to realize the fast solution of CCSD (named KH-CCSD). During the process of optimization, the search space representation and fitness function for the KH algorithm are constructed to satisfy the solution of CCSD. The optimal signal timing plan is obtained by an iterated search of krill swarm in a multi-dimensional time-varying space cooperatively constrained by traffic supplies and demands. The convergence and effectiveness of KH-CCSD are validated by comparing experiments, in which the convergence of KH-CCSD is tested by different initializations and KH-CCSD is compared with the Webster method and capacity-aware backpressure (CABP) control under unsaturated, saturated, and oversaturated conditions. The experiments results show that KH-CCSD performs a fast convergence and KH-CCSD is superior to the Webster method and CABP. As a result, KH-CCSD could satisfy the application of CCSD under all traffic conditions. INDEX TERMS KH-CCSD, krill herd algorithm, signal cooperative control, traffic supply and demand.

show abstract

Section: Related Workmentioning

confidence: 99%

Krill Herd Algorithm for Signal Optimization of Cooperative Control With Traffic Supply and Demand

Cao

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The iterative search is ended when the termination criteria are met. Wang, Deb, and Thampi (2015) developed a discrete krill herd method for flexible job shop scheduling. Puongyeam, Pongcharoen, and Vitayasak (2014) developed a discrete krill herd for scheduling in the capital goods industry which used the objective function shown in equation (1).…”

Section: Krill Herd (Kh) Algorithmmentioning

confidence: 99%

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

Chansombat

Musikapun

Pongcharoen

et al. 2018

International Journal of Production Research

View full text Add to dashboard Cite

Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four datasets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size.

show abstract

“…Regarding the procedure's internal parameters, the tuning of the KHA is described in papers: [24], [25] and [26], while publications [17] and [16] introduce some modifications into the algorithm. The KHA procedure has been verified for application within optimization problems in the case of discrete input data [27], while a parallel version of this procedure is put forward in [28]. Furthermore, it has been applied in medical tasks [29], for data base domains [30], in mechanism and machine theory [31], in clustering tasks [32], [33], and also in neural learning processes [34].…”

Section: Optimisation Based On Krill Herd Algorithmmentioning

confidence: 99%

On the Use of Nature Inspired Metaheuristic in Computer Game

Kowalski¹,

Łukasik²,

Charytanowicz³

et al. 2017

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-This paper describes the use of a new swarm-based metaheuristic, namely Krill Herd Algorithm (KHA), in computer gaming. In this work, KHA is employed to find a bots movement strategy in a computer racing game. The complete algorithm is implemented using a Unity Engine in C# language. Herein, the triggering of the metaheuristic optimization task was conducted by the way of a KHA internal parameter investigation. In this approach, the goal of the race (the KHA evaluation function) for both the human and computer player is to finish a lap in the shortest time possible.

show abstract

A Discrete Krill Herd Method with Multilayer Coding Strategy for Flexible Job-Shop Scheduling Problem

Cited by 11 publications

References 69 publications

Krill Herd Algorithm for Signal Optimization of Cooperative Control With Traffic Supply and Demand

Krill Herd Algorithm for Signal Optimization of Cooperative Control With Traffic Supply and Demand

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

On the Use of Nature Inspired Metaheuristic in Computer Game

Contact Info

Product

Resources

About