Multi-objective optimization of power and heating system based on artificial bee colony

Atashkari, Kazem; Nariman-Zadeh, N.; Ghavimi, A.R.; Mahmoodabadi, Mohammad Javad; Aghaienezhad, F.

doi:10.1109/inista.2011.5946159

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…Gonzlez-lvarez et al (2011) proposed the application of a multi-objective ABC to solve the motif discovery problem and applied it to the specific task of discovering novel transcription factor binding sites in DNA sequences. Atashkari et al (2011) introduced a multi-objective ABC for optimization of power and heating system. Arsuaga-Rios et al ( 2011) presented a multi-objective ABC for scheduling experiments across the grid and the well-known deadline budget constraint algorithm from Nimrod-G and the workload management system scheduler from the middle-ware gLite (lightweight middle ware for grid computing) were compared with the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

Omkar

Senthilnath

Khandelwal

et al. 2016

International Journal of Applied Metaheuristic Computing

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Cement is the most widely used additive in soft soil stabilization due to its high strength and availability. The cement content and curing time have a direct influence on the stabilization cost and hence it is prudent to minimize these variables to achieve optimality. Thus, it is a classical multi-objective optimization problem to find the optimum combination of cement content used and the curing time provided to achieve the target strength. This paper brings out the use of Vector Evaluated Artificial Bee Colony (VEABC) algorithm, a multi-objective variant of Artificial Bee Colony (ABC) technique, for the problem on hand. VEABC is a swarm intelligence algorithm, which employs multiple swarms to handle the multiple objectives and the information migration between these swarms ensures a global optimum solution is reached. Due to the stochastic nature of ABC algorithm, the resulting Pareto Curve will cover a good range of data with smooth transition. The Pareto fronts obtained for target strength could be used as calibration charts for scheduling the soft soil stabilization activities.

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Section: Introductionmentioning

confidence: 99%

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

Omkar

Senthilnath

Khandelwal

et al. 2016

International Journal of Applied Metaheuristic Computing

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“…Also, Hammache et al have utilized a multi-objective self-adaptive algorithm for optimizing the modified CGAM problem [17]. Atashkari et al have applied a multi-objective artificial bee colony algorithm to optimize the same CGAM problem [18]. Soltani et al have implemented genetic algorithms for multiobjective optimization of a CGAM problem [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of power and heating systems based on a new hybrid algorithm

Mahmoodabadi

Ghavimi

Mahmoudi

2015

Alexandria Engineering Journal

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A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is applied on a base case cogeneration optimization problem called the modified CGAM problem with two objective functions. The first objective function, the exergetic efficiency, should be maximized and the second one is the total cost rate that should be minimized. The effects of important parameters, such as equivalence ratio, emission, and unit cost of fuel are studied on the exergetic and economic performance of the system. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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A Multi-objectives Genetic Algorithm Clustering Ensembles Based Approach to Summarize Relational Data

Alfred

Chiye

Lim

et al. 2016

Communications in Computer and Information Science

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Multi-objective optimization of power and heating system based on artificial bee colony

Cited by 10 publications

References 6 publications

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

Optimization of power and heating systems based on a new hybrid algorithm

A Multi-objectives Genetic Algorithm Clustering Ensembles Based Approach to Summarize Relational Data

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