Job Shop Scheduling with the Best-so-far ABC

Banharnsakun, Anan; Sirinaovakul, Booncharoen; Achalakul, Tiranee

doi:10.1016/j.engappai.2011.08.003

Cited by 113 publications

(34 citation statements)

References 25 publications

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“…The swarm updating in ABC is due to two processes namely, the variation process and the selection process which are responsible for exploration and exploitation, respectively. The ABC algorithm has successfully been tested on almost all domains of science and engineering like electronics engineering (Chidambaram and Lopes 2009;Kavian et al 2012), electrical engineering (Jones and Bouffet 2008;Nayak et al 2009;Sulaiman et al 2012), computer science engineering (Karaboga and Cetinkaya 2011;Lam et al 2012;Lei et al 2010), mechanical engineering (Banharnsakun et al 2012;Pawar et al 2008;Xu and Duan 2010), civil engineering (Akay and Karaboga 2012;Li et al 2011;Mandal et al 2012), medical pattern classification and clustering problems (Karaboga et al 2008) and mathematical graph problems (Xing et al 2007;Singh 2009;Yeh and Hsieh 2011). Many of the recent modifications and applications of ABC algorithm can be studied in Bansal et al (2013b).…”

Section: Introductionmentioning

confidence: 98%

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Bansal

Jadon

Tiwari

et al. 2014

Int J Syst Assur Eng Manag

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Optimal power flow (OPF) is one of the most requisite tools for power system operation analysis. This problem has a complex mathematical formulation which is relatively hard to solve. This paper presents a swarm intelligence-based approach to solve the OPF problem. The proposed approach describes the use of a modified artificial bee colony (ABC) algorithm called ABC with global and local neighborhoods (ABCGLN) to determine the optimal settings of OPF control variables. ABCGLN is a recent modified version of basic ABC algorithm that can handle non-differentiable, non-linear, and multi modal objective functions. The ABCGLN approach is tested here on the standard IEEE 30-bus test system with three different objective functions for minimizing quadratic fuel cost function, piecewise quadratic cost function and quadratic cost function with valve point effects. The simulation results demonstrate the potential of ABCGLN algorithm of finding effective and robust quality solutions to solve OPF problem with various objective functions for the considered system as compared to those available in the literature.

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Section: Introductionmentioning

confidence: 98%

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Bansal

Jadon

Tiwari

et al. 2014

Int J Syst Assur Eng Manag

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“…Recent Artificial Bee Colony applications on the resolution of scheduling problems can be found in, e.g. [55][56][57][58].…”

Section: Meta-heuristics Applications For Schedulingmentioning

confidence: 99%

Self-Optimization module for Scheduling using Case-based Reasoning

Pereira

Madureira

2013

Applied Soft Computing

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a b s t r a c tMetaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution.This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems.The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions.After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred.It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

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“…Huang and Lin [12] presented an Open-Shop scheduling problem work "with an idle-time-based filtering scheme", a system that can automatically adapt their behavior stopping the search in solutions with insufficient fitness, decreasing "time-cost for the remaining partial solution time-cost". Banharnsakun et al [13] proposed an effective scheduling method based on best-so-far ABC for solving the Job-Shop Scheduling Problem. The authors biased the solution direction toward the best-so-far solution rather a neighboring solution as proposed in the original ABC method.…”

Section: B Bee Based Approaches For Scheduling Resolutionmentioning

confidence: 99%

Towards Scheduling Optimization through Artificial Bee Colony Approach

Madureira

Pereira

Abraham

2013

2013 World Congress on Nature and Biologically Inspired Computing

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Abstract-In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

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Job Shop Scheduling with the Best-so-far ABC

Cited by 113 publications

References 25 publications

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Self-Optimization module for Scheduling using Case-based Reasoning

Towards Scheduling Optimization through Artificial Bee Colony Approach

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