A new method for multi-objective in core fuel management optimization using biogeography based algorithm

Khoshahval, Farrokh; Zolfaghari, A.; Minuchehr, A.

doi:10.1016/j.anucene.2014.06.047

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…It was proposed by Simon based on the migration behavior of the biological species among habitats in the nature. Until now, the BBO method has been successfully applied to solve many engineering optimization problems . The BBO simulates the migration process by using mathematical models in the biogeography science.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Jalili

Hosseinzadeh

2018

Structural Design Tall Build

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This paper presents a hybrid BBO-DE algorithm by hybridizing biogeography-based optimization (BBO) and differential evolution (DE) methods for optimum design of truss structures with continuous and discrete variables. In BBO-DE, the migration operator of BBO method serves as a local exploiter mechanism during the search process. Besides, DE has a role of the global exploration by performing multiple search directions in the search space to preserve more diversity in the population. By embedding of DE algorithm in BBO method as a mutation mechanism, the balance between the exploration and exploitation abilities is further improved. The comparative results with some of the most recently developed methods demonstrate the fast convergence properties of the proposed algorithm and confirm its effectiveness to solve optimum design problems of truss structures with continuous and discrete variables. KEYWORDSbiogeography-based optimization, continuous, differential evolution, discrete, optimum design, truss structures | INTRODUCTIONIn recent years, increasing the consumption of materials in the building sector and the limitation of natural resources have intensified competitions among the structural engineers to such an extent that attaining optimum designs for structures is more vital now than ever before. Structural optimization has achieved a great advance as a result of modern computers entering the stage. By using today's computational machines, a large-scale structure with complex geometry and thousands of members can be iteratively processed to get a more economical design. For such structures, the number of design variables is high and the optimum design procedure may require huge computational effort. As a result, developing efficient optimization techniques to achieve a fast convergence rates with more accurate results is one of the active research topics in the field of structural optimization. In the sizing of truss structures, the cross-sectional areas of structural members are considered as design variables, and the design constraints are some limitations imposed on the stresses and displacements as well as free vibrational frequencies. The objective function is generally considered as to minimize the weight or volume of the structure. However, there are many critical factors that can affect the objective function or costs of a structure.From the optimization point of view, the sizing of truss structures can be classified into two main types based on design variables: continuous and discrete. Mathematical optimization techniques have some features that are later found to be not very much suitable for practical structural design optimization problems, [1] such as assuming of continuous design variables, requiring gradient computations of the objective function and constraints, and depending on the initial estimate of solution vector. These methods have some serious difficulties when it is required to assign cross sections from a discrete set of practically available sections. Moreover, when both of the nodal ...

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Section: Introductionmentioning

confidence: 99%

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Jalili

Hosseinzadeh

2018

Structural Design Tall Build

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show abstract

“…7), 2# BHS with g ¼ 0.5 in the "cold" heating season would be subjected to a fuel deficits of [1988.15, 2074.13] Fig. 8 27,1925.82] tonnes. Because there would be nearly no biofuel deficits at the rich biomass-harvest level, it obviously reflects that once the local biomass-harvest level is not rich, the deficits would potentially occur in the BHSs.…”

Section: Biofuel Deficit Allotment Patternmentioning

confidence: 99%

A multi-fuel management model for a community-level district heating system under multiple uncertainties

Zhuang

Shi

et al. 2017

Energy

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A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm

Luan

Liu

2016

Neurocomputing

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A new method for multi-objective in core fuel management optimization using biogeography based algorithm

Cited by 5 publications

References 35 publications

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

A multi-fuel management model for a community-level district heating system under multiple uncertainties

A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm

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