Plant intelligence based metaheuristic optimization algorithms

Akyol, Sinem; Alataş, Bilal

doi:10.1007/s10462-016-9486-6

Cited by 194 publications

(107 citation statements)

References 74 publications

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“…The local search approach relies on many iterations of a process being used to find neighboring solutions to the latest solution, and thus seeking to continually improve upon the current solution in local steps [12]. One advantage of metaheuristic algorithms is their ability, via their stochastic properties, to avoid becoming trapped in a local optimum [13]. Every metaheuristic step requires a balance to be found between the concepts of diversification and intensification.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

“…The role of constraints (8) and (9) is to limit the quantity of vaccine to no more than the capacity of the vehicles used in the network. Constraints (10) and (11) set the route of the refrigerated vehicles k and l to PHOj and CUPm by constraints (10) and (11), on at least one time both PHOj and CUPm while constraints (12) and (13) ensure that PHOj and CUPm receive the vaccines. Sub-tours are prevented within the network by the remaining constraints.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Sujaree¹

2020

IJMO

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In this research study, a novel metaheuristic approach using nanotechnology is proposed, known as Artificial Carbon Nanotube Synthesis Optimization (ACNSO), in order to develop a vaccine cold chain network in north of Thailand. The scope of the study emphasizes Area 1 of the Office Disease Prevention and Control in the Chiang Mai region. Vaccines must be transported both to the Provincial HealthOffices and hospitals in the region. This study seeks to arrange the transportation routes involved in order to achieve the shortest possible total distance. The algorithm must first of all assess the travel conditions between each point in the network, and then generate the starting solution. Efficient solutions to this problem will cut the total processing time. The study then made a comparison between the results produced by ACNSO algorithm and those of other algorithms used in earlier studies. Full factorial design was the statistical approach used to evaluate the optimal parameters for the algorithm. The experiment was designed to examine the various factors which influence the algorithm performance. The results showed that ACNSO algorithm found the best solution in experimental algorithms and 3 rd processing time.Index Terms-Vaccine cold chain network, metaheuristic approach, full factorial design, nanotechnology, artificial carbon nanotube synthesis optimization.

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Section: Metaheuristic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Sujaree¹

2020

IJMO

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“…Algorithm 2 illustrates the proposed Jaya based HEMS. [35]. The strawberry plant is generally known for its characteristic aroma, bright red color, juicy texture and its sweetness.…”

Section: Jaya Algorithmmentioning

confidence: 99%

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Dahunsi

Javaid

et al. 2018

Energies

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Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

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“…Several plant intelligence-based inspired optimization algorithms have been proposed in [44]. Authors in [45] propose a numerical SBA to resolve steady multi-variable problems.…”

Section: Sbamentioning

confidence: 99%

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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Plant intelligence based metaheuristic optimization algorithms

Cited by 194 publications

References 74 publications

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

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