A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Taşgetiren, M. Fatih; Pan, Quan-Ke; Suganthan, Ponnuthurai Nagaratnam; Chen, Angela H-L

doi:10.1016/j.ins.2011.04.018

Cited by 235 publications

(74 citation statements)

References 42 publications

(42 reference statements)

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“…In this study, a new crossover operator is introduced by using an artificial chromosome generated from a weighted simple mining gene structure. Tasgetiren et al (2011) and SLS of Dubois-Lacoste et al (2011). We also present four simple local search based algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Local search methods for the flowshop scheduling problem with flowtime minimization

Pan

Ruíz

2012

European Journal of Operational Research

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121

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Flowshop scheduling is a very active research area. This problem still attracts a considerable amount of interest despite the sheer amount of available results. Total flowtime minimization of a flowshop has been actively studied and many effective algorithms have been proposed in the last few years. New best solutions have been found for common benchmarks at a rapid pace. However, these improvements many times come at the cost of sophisticated algorithms. Complex methods hinder potential applications and are difficult to extend to small problem variations. Replicability of results is also a challenge. In this paper, we examine simple and easy to implement methods that at the same time result in state-of-the-art performance. The first two proposed methods are based on the well known Iterated Local Search (ILS) and Iterated Greedy (IG) frameworks, which have been applied with great success to other flowshop problems. Additionally, we present extensions of these methods that work over populations, something that we refer to as population-based ILS (pILS) and population-based IG (pIGA), respectively. We calibrate the presented algorithms by means of the Design of Experiments (DOE) approach. Extensive comparative evaluations are carried out against the most recent techniques for the considered problem in the literature. The results of a comprehensive computational and statistical analysis show that the presented algorithms are very effective. Furthermore, we show that, despite their simplicity, the presented methods are able to improve 12 out of 120 best known solutions of Taillard's flowshop benchmark with total flowtime criterion.

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Section: Literature Reviewmentioning

confidence: 99%

Local search methods for the flowshop scheduling problem with flowtime minimization

Pan

Ruíz

2012

European Journal of Operational Research

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121

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“…Some authors (Han et al, 2012;Wu, Qian, Ni, & Fan, 2012;Karaboga & Ozturk, 2011) propose random generation of the food sources (solutions) to guarantee diversification of solutions. Some others propose generating at least one of the solutions by a heuristic procedure in order to obtain one food source of a certain quality (Tasgetiren, Pan, Suganthan, & Chen, 2011b). However, in Liu and Liu (2013), a GRASP based on an NEH algorithm (Nawaz, Enscore, & Ham, 1983) is used to generate all food sources in order to guarantee an initial swarm of quality and diversity.…”

Section: Initializationmentioning

confidence: 99%

An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization

Ribas

Companys

Tort‐Martorell

2015

Expert Systems with Applications

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This paper presents a high performing Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with flow time criterion. To develop the proposed algorithm, we considered four strategies for the food source phase and two strategies for each of the three remaining phases (employed bees, onlookers and scouts). One of the strategies tested in the food source phase and one implemented in the employed bees phase are new. Both have been proved to be very effective for the problem at hand. The initialization scheme named HPF2(λ,µ) in particular, which is used to construct the initial food sources, is shown in the computational evaluation to be one of the main procedures that allow the DABC_RCT to obtain good solutions for this problem. To find the best configuration of the algorithm, we used Design of Experiments (DOE).This technique has been used extensively in the literature to calibrate the parameters of the algorithms but not to select its configuration. Comparing it with other algorithms proposed for this problem in the literature demonstrates the effectiveness and superiority of the DABC_RCT.

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“…Due to its simplicity and ease of implementation, the ABC algorithm has captured much attention and has been applied to solve many engineering optimization problems [12][13][14][15][16][17][18][19][20][21]. Karaboga [12] proposed a new method based on the ABC algorithm was used for designing digital infinite impulse response (IIR) filters, and its performance was compared with those of other techniques.…”

Section: Introductionmentioning

confidence: 99%

Binary Artificial Bee Colony Optimization for GENCOs' Profit Maximization under Pool Electricity Market

Mandala¹,

Gupta²

2014

IJCA

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This paper proposes GENCOs' profit maximization using Binary Article Bee Colony Optimization based on global best parameters (GbBABC). The optimal rival bidding strategy is employed to maximize GENCOs' profit. Monte Carlo (MC) simulation has been used to predict the bidding behavior of the rivals. In this paper, a bi-level optimization problem has been proposed to obtain the optimal bidding strategy of a supplier in which lower level problem represents the market clearing process of the system operator (SO) and the upper level optimization problem represents the supplier's profit maximization function, which is a non-linear function. In the proposed algorithm, global best parameter was incorporated into BABC algorithm, which makes the exploitation capacity improved and convergence speed quickened. At the same time, in order to maintain the population diversity the bit mutation operator is also performed. The feasibility of the proposed approach is analyzed on IEEE 30-bus system and IEEE-57 bus system. Results obtained using the GbBABC algorithm have been compared with those obtained using standard Artificial Bee Colony (ABC) optimization, global best guided ABC (GbABC) and global best distance guided ABC (GbdABC).

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A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Cited by 235 publications

References 42 publications

Local search methods for the flowshop scheduling problem with flowtime minimization

Local search methods for the flowshop scheduling problem with flowtime minimization

An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization

Binary Artificial Bee Colony Optimization for GENCOs' Profit Maximization under Pool Electricity Market

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A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Cited by 235 publications

References 42 publications

Local search methods for the flowshop scheduling problem with flowtime minimization

Local search methods for the flowshop scheduling problem with flowtime minimization

An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization

Binary Artificial Bee Colony Optimization for GENCOs&apos; Profit Maximization under Pool Electricity Market

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Binary Artificial Bee Colony Optimization for GENCOs' Profit Maximization under Pool Electricity Market