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programming techniques. Selbas et al. [13] used genetic algorithm
(GA) for optimal design of STHEs, in which pressure drop was
applied as a constraint for achieving optimal design parameters.
Caputo et al. [14] carried out heat exchanger design based on
economic optimization using GA. They minimized the total cost of
the equipment including capital investment and the sum of dis-
counted annual energy expenditures related to pumping. Ponce-
Ortega et al. [15] also have used genetic algorithms for the
optimal design of STHEs. The approach uses the BelleDelaware
method for the description of the shell-side flow with no simplifi-
cations. Several other investigators also used strategies based
on genetic optimization algorithms [15e22] for various objectives
like minimum entropy generation [19] and minimum cost of STHEs
[15e18,21,22] to optimize heat exchanger design. Patel and Rao [23]applied particle swarmoptimization (PSO) forminimization of total
annual cost of STHEs. In that study the main focus was the analyses
of the heat exchangers principles, while the optimization approach
was just a tool. Shahin et al. [24] presented an artificial bee colony
(ABC) algorithm for optimization of a shell ant tube heat exchanger.
RecentlyMariani et al. [25] used a PSOmethod to optimal designing
of a shell and tube heat exchanger. They combined a quantum
particle swarm optimization (QPSO) approach with Zaslavskii [26]
chaotic map sequences (QPSOZ) to shell and tube heat exchanger
optimization based on theminimization fromeconomic view point.
Some others tried to optimize a variety of geometrical and opera-
tional parameter of the STHEs. However, there is a need to inves-
tigate the potential of application of non-traditional optimization
techniques. Biogeography-based optimization (BBO) is one such
technique and the same is investigated in the present work for its
effectiveness.
BBO is a new and powerful optimization technique proposed by
Simon [27], which has been never used in thermal and energy
systems optimization so far. Biogeography is the study of the
geographical distribution of biological organisms. Themindset of the
engineer is that we can learn from nature. This motivates the
application of biogeography to optimization problems.Geographical
areas that arewell suited as residences for biological species are said
to have a high habitat suitability index (HSI). The variables that
characterize habitability are called suitability index variables (SIVs).
SIVs can be considered the independent variables of the habitat, and
HSI can be considered the dependent variable. Habitats with a high
HSI tendtohave a largenumberof species,while thosewitha lowHSI
have a small number of species. Habitats with a high HSI havemany
species that emigrate to nearby habitats, simply by virtue of the large
number of species that they host. Habitats with a high HSI have low
species immigration rate because they are already nearly saturated
with species. Therefore, high HSI habitats are more static in their
species distribution than low HSI habitats. Biogeography is nature’s
way of distributing species, and is analogous to general problem
solutions. Suppose that we are presented with a problem and some
candidate solutions. The problem can be in any area of life (engi-
neering, economics,medicine, business,urbanplanning, sports, etc.),
as long aswe have a quantifiablemeasure of the suitability of a given
solution. A good solution is analogous to an island with a high HSI,
and a poor solution represents an island with a low HSI. High HSI
solutions resist change more than low HSI solutions. By the same
token, high HSI solutions tend to share their features with low HSI