Elephant herding optimization based PID controller tuning

Gupta, Sakshi; Singh, V. P.; Singh, Shailendra; Prakash, Tapan; Rathore, Natwar Singh

doi:10.19101/ijatee.2016.324005

Cited by 39 publications

(14 citation statements)

References 6 publications

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“…Wang et al [48] designed the EHOA for solving the global optimization tasks. Gupta et al [49] suggested the EHOA to optimize the PID controller design. Ahmed et al [50] suggested an improved Elephant Swarm Optimization Algorithm for community detection problem.…”

Section: B Elephant Heard Optimization Algorithm (Ehoa)mentioning

confidence: 99%

A modified elephant herd optimization algorithm to solve the single machine scheduling problems

Marichelvam¹,

Mani²

2019

IJEAT

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This study reports the single machine scheduling problem for finding the minimum the total weighted tardiness. As the problem is non-deterministic polynomial-time hard (NP-hard), the problem might not be answered by the exact solution techniques. Henceforth, different heuristics and meta-heuristics were recommended by different practitioners to tackle the problem. A modified elephant herd optimization algorithm (MEHOA) is investigated in the present work to solve the single machine total weighted tardiness scheduling problem (SMTWTSP). The performance of the anticipated approach is studied with the test instances available in the OR library. The outcomes are compared with several different algorithms offered in the literature and indicate the efficacy of the developed methodology.

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Section: B Elephant Heard Optimization Algorithm (Ehoa)mentioning

confidence: 99%

A modified elephant herd optimization algorithm to solve the single machine scheduling problems

Marichelvam¹,

Mani²

2019

IJEAT

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“…Moreover, it was also considered in applications such as proportional-integral-derivative control tuning [32] and quality of web service composition [33].…”

Section: Original Eho Algorithmmentioning

confidence: 99%

Elephant Herding Optimization for Energy-Based Localization

Correia¹,

Beko²,

Cruz³

et al. 2018

Preprint

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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

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“…Moreover, it was also considered in applications such as proportional–integral–derivative control tuning [34] and quality of web service composition [35]. …”

Section: Elephant Herding Optimizationmentioning

confidence: 99%

“…EHO has been applied to several optimization benchmark problems [33] and real life applications showing promising results in finding optimal solutions [34,35]. To the best of our knowledge, this method has not been used to solve energy based localization problems.…”

Section: Introductionmentioning

confidence: 99%

Elephant Herding Optimization for Energy-Based Localization

Correia

Beko

Cruz

et al. 2018

Sensors

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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

show abstract

Elephant herding optimization based PID controller tuning

Cited by 39 publications

References 6 publications

A modified elephant herd optimization algorithm to solve the single machine scheduling problems

A modified elephant herd optimization algorithm to solve the single machine scheduling problems

Elephant Herding Optimization for Energy-Based Localization

Elephant Herding Optimization for Energy-Based Localization

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