A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

Narendhar, S.; Amudha, T.

doi:10.5121/ijpla.2012.2401

Cited by 17 publications

(11 citation statements)

References 11 publications

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“…Biological studies have shown that the foraging process includes four steps as shown in Figure 3 which includes swarming, reproduction and elimination-dispersal (Tang et al, 2006). The E-coli bacteria can have its movement as a 'run' which is moving in a particular direction whereas a 'tumble' denotes that E-coli can change its direction (Narendhar and Amudha, 2012;Panigrahi et al, 2011;Passino, 2002;Kim et al, 2007).…”

Section: Conceptual Representationmentioning

confidence: 99%

A survey on cloud-based solutions for cloud manufacturing

Brintha

Benedict

2018

IJCAET

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Cloud computing is becoming an emerging technology that is changing the traditional industry and enterprises to do their work in a new way using dynamically scalable, virtualised heterogeneous resources over the internet. Nowadays, cloud manufacturing is evolving as a new technology that transforms the traditional manufacturing business model to effective reconfigurable production lines, allows effective load balancing to dynamic customer demands and reduces the time to market. In cloud manufacturing, the resources that are geographically distributed across several sectors are incorporated as cloud-based services and they are provided to users in a centralised manner. The resources can be provisioned to the users of cloud as pay as you go model depending on the user requirements.

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Section: Conceptual Representationmentioning

confidence: 99%

A survey on cloud-based solutions for cloud manufacturing

Brintha

Benedict

2018

IJCAET

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“…In order to explore separately the local and global search proprieties of BFOA, researchers hybridized it with other algorithms, applied it to several real world problems and proved its effectiveness over many works, e.g., mathematical modeling for solving simultaneous equation problems [19], solving scheduling problems [12], mechanical problems [2], solar PV modeling [20], and numerical optimization [1]. More hybridization is observed in [18,21].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

2017

J Inf Process Syst

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Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

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“…In this way, appliances' waiting time is minimized, and UC is achieved maximally. This is the multi-objective problem; several authors handle it using different approaches, as mentioned in the literature [12][13][14][15][16][17]. Here, it is handled by the metaheuristics for scheduling the residential area loads in order to reduce the electricity cost and maximize the UC.…”

Section: Uc Maximizationmentioning

confidence: 99%

“…In the literature, many DSM techniques are proposed [8][9][10][11] to achieve the aforementioned objectives. However, UC is not considered in most of these techniques, like [8,10,[12][13][14][15][16][17]. In these works, [11,18,19] aim to reduce the electricity cost, and [20,21] focus on minimizing the aggregated power consumption using integer linear programming and mixed integer linear programming.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

et al. 2017

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Abstract:In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

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A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

Abstract: Bio-Inspired computing is the subset of Nature-Inspired computing.

Cited by 17 publications

References 11 publications

A survey on cloud-based solutions for cloud manufacturing

A survey on cloud-based solutions for cloud manufacturing

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

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