A new adaptive Cuckoo search algorithm

Naik, Manoj Kumar; Nath, Maheshwari Rashmita; Wunnava, Aneesh; Sahany, Siddharth; Panda, Rutuparna

doi:10.1109/retis.2015.7232842

Cited by 44 publications

(22 citation statements)

References 25 publications

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“…In the literature, a lot of optimization schemes have been presented to schedule electrical devices optimally. In case of EHO, it has distinguished local search abilities [6], whereas, ACS proves to be efficient in finding optimal results in less computational time [25]. In order to enable parallel interpolation and extrapolation, a dire need for an algorithm emerges which can maximize the benefits of scheduling.…”

Section: Proposed Schemementioning

confidence: 99%

“…The ACS algorithm is a heuristic technique and inspired by the thinking of cuckoo bird, which usually explores the search space by using the Levy step [23]. The Levy step used is taken from the Levy distribution proposed by Mantegna algorithm [25]. The authors assimilate a step size proportional to the fitness of a nest in search space.…”

Section: Ehomentioning

confidence: 99%

“…The authors assimilate a step size proportional to the fitness of a nest in search space. The step size is designed as [25]:…”

Section: Ehomentioning

confidence: 99%

“…Equation (5), shows that in nature the step size is adaptive and uses the fitness value to decide the next step size. It is modeled as [25]:…”

Section: Ehomentioning

confidence: 99%

“…Equation 6provides the new search space from current solution to the ACS algorithm. The other advantage of ACS is that there is no need for defining the initial parameter [25]. It is faster than the cuckoo search algorithm.…”

Section: Ehomentioning

confidence: 99%

See 4 more Smart Citations

Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization

et al. 2018

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In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm called hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. A regional based time of use (ToU) tariff is used to make the system effective for different types of regions. Moreover, this enables the consumers to act according to the regional environment. The coordination can play a very important role in cost reduction as well as in consumer comfort maximization. The coordination is incorporated among the electrical devices by using cooperative game theory (GT) and dynamic programming (DP). Extensive simulations are performed to show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization.

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Section: Proposed Schemementioning

confidence: 99%

Section: Ehomentioning

confidence: 99%

“…The authors assimilate a step size proportional to the fitness of a nest in search space. The step size is designed as [25]:…”

Section: Ehomentioning

confidence: 99%

“…Equation (5), shows that in nature the step size is adaptive and uses the fitness value to decide the next step size. It is modeled as [25]:…”

Section: Ehomentioning

confidence: 99%

Section: Ehomentioning