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Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm

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Abstract

In any factory or industry the high level noise can be very harmful to the employees. As investigated by Occupational Safety and Health Act of 1970, the high level noise not only causes physiological ailments in employees but also causes harmful environment in the neighborhood. Therefore it becomes essential to control the noise levels in any manufacturing plant or industry. This can be achieved by optimal allocation of noise equipment which is quite not easy to recognize the exact location. In this study a shuffled frog-leaping algorithm (SFLA) with modification is applied to identify optimal locations for equipment in order to reduce noise level in multi noise plant. Comparatively, SFLA is a recent addition to the family of nontraditional population based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real time problems but it limits in convergence speed. In order to improve its performance, the frog with best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named as improved local search in SFLA. The simulated results defend the efficacy of the proposal when compared with the differential evolution, GA and SFL algorithms.

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Authors and Affiliations

  1. Amity University Rajasthan, Jaipur, India

    Tarun Kumar Sharma

  2. Department of Applied Science and Engineering, IIT Roorkee, Roorkee, India

    Millie Pant

Authors
  1. Tarun Kumar Sharma
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  2. Millie Pant
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Correspondence to Tarun Kumar Sharma.

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Sharma, T.K., Pant, M. Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm. Int J Syst Assur Eng Manag 9, 43–51 (2018). https://doi.org/10.1007/s13198-016-0466-7

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  • DOI: https://doi.org/10.1007/s13198-016-0466-7

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