Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Tan, Choo Jun; Neoh, Siew Chin; Lim, Chee Peng; Hanoun, Samer; Wong, Wai Peng; Loo, Chu Kong; Zhang, Li; Nahavandi, Saeid

doi:10.1007/s10845-016-1291-1

Cited by 25 publications

(11 citation statements)

References 73 publications

(71 reference statements)

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“…In terms of potential applications, the proposed models can be applied to diverse real-life single and multiobjective optimization problems, such as job scheduling [68], design optimization [37], RFID network planning [36], radiation therapy treatment planning [40], optimal parameter identification [39,41,42], feature selection [28,43,49,60,[69][70][71], colour image segmentation [46], image retrieval and classification [42,44]. As an example, the proposed models can be employed to identify the most significant discriminative features for facial and bodily expression [49,60], skin cancer [69], heart disease [70], and brain tumour classification [71].…”

Section: Discussionmentioning

confidence: 99%

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

Pandit

Zhang

Chattopadhyay

et al. 2018

Knowledge-Based Systems

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The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions.

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Section: Discussionmentioning

confidence: 99%

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

Pandit

Zhang

Chattopadhyay

et al. 2018

Knowledge-Based Systems

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“…Experimental setup. The proposed Social Spider Optimization algorithm with Differential Mutation operator (SSO-DM) compared with swarm intelligence optimization algorithms, such as PSO [20], and evolutionary algorithms, IGA [15], DE [42], evolutionary algorithm-based ensemble model [35] using the mean and standard deviation to compare their optimal performance. Among them, the SSO is the original algorithm of SSO-DM, it does not have differential mutation operation.…”

Section: 1mentioning

confidence: 99%

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

Zhou¹,

Zhou²,

Zhao³

2021

Journal of Industrial &Amp; Management Optimization

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The job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. The problem has captured the interest of a significant number of researchers, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. In this paper, a hybrid social-spider optimization algorithm with differential mutation operator is presented to solve the job-shop scheduling problem. To improve the exploration capabilities of the social spider optimization algorithm (SSO), we incorporate the DM operator (a mutation operator taken from the deferential evolutionary (DE) algorithm) into the framework of the female cooperative operator. The experimental results show that the proposed method effectiveness in solving job-shop scheduling compared to other optimization algorithms in the literature.2010 Mathematics Subject Classification. Primary: 78M50, 90C27.

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“…The evolutionary process ends after some termination criteria are met. These synergetic combinations of population-based, fitness-based, and variation-driven search have reported success in many complex optimisation problems (Tan, Lim and Cheah, 2013; Lim et al , 2015a, b, 2016; Tan et al , 2017). Meanwhile, the literature of GA runs a long list of variance diverging from its original, yet maintaining the novelty of GA characteristics.…”

Section: Preliminariesmentioning

confidence: 99%

“…This paper presents a soft computing technique, which comprises an evolutionary algorithm (EA), i.e., modified micro genetic algorithm (MmGA) , coupled with a decision tree (DT)-based classifier, namely, C4.5 for the classification and optimisation of system. The MmGA works well in MOP context as shown by a series of previous successes Tan et al, 2014;Tan, Hanoun and Lim, 2015;Tan et al, 2017). In order to evaluate the proposed soft computing model, an empirical-based case study is conducted.…”

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confidence: 98%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach -A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.Findings -The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value -A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

Cited by 25 publications

References 73 publications

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

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