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solutions. Poor solutions accept a lot of new features from good
solutions. This new approach for solving problem is known as
biogeography-based optimization (BBO). BBO mainly works based
on the twomechanisms. These aremigration andmutation. BBO has
certain features in common with other biology-based algorithms.
Like GAs and PSO, BBO has a way of sharing information between
solutions. GA solutions ‘‘die” at the end of each generation, while
PSOand BBOsolutions survive forever (although their characteristics
change as the optimization process progresses). PSO solutions are
more likely to clump together in similar groups, while GA and
BBO solutions do not necessarily have any built-in tendency to
cluster [27].
As seen above, studies on optimization of energy systems with
BBO algorithm in the literature was not found. In this paper, BBO
algorithm was applied to the new field. The application of the BBO
algorithm in the shell and tube heat exchanger optimization design
as different from artificial intelligence methods available in the
literature is investigated. This algorithm was successfully applied
for design and economic optimization of shell and tube heat
exchangers. This study has provided new and powerful method-
ology in the optimization of shell and tube heat exchangers. Based
on proposed method, a full computer code was developed for
optimal design of shell and tube heat exchangers and three
different test cases are solved by it to demonstrate the effectiveness
and accuracy of the proposed algorithm. Results of the presented
algorithms are compared with the previously published results
obtained by using other optimization techniques and showed that
the proposed method is very accurate, quick and economic method
for optimal design of shell and tube heat exchangers.
2. Overview of BBO technique
Biogeography-based optimization technique (BBO) [27] has been
developed based on the theory of Biogeography. BBO concept is
mainly based on Migration and Mutation. The concept and math-
ematical formulation of Migration and Mutation are given below:
2.1. Migration
This BBO algorithm [27] is similar to other population-based
optimization techniques where population of candidate solutions is
represented as vector of real numbers. Each real number in the array
is considered as one SIV. Fitness of each set of candidate solution is
evaluated using SIV. In BBO, a termHSI is usedwhich is analogous to
fitness function of other population-based techniques, to represent
the quality of each candidate solution set. High HSI solutions repre-
sent better quality solution and low HSI solutions represent inferior
solution in optimization problem. The emigration and immigration
rates of each solution are used to probabilistically share information
between habitats. Using Habitat Modification Probability each solu-
tion ismodified based on other solutions. Immigration rate, ls of each
solution is used to probabilistically decide whether or not to modify
each suitability index variable (SIV) in that solution. After selecting
the SIV for modification, emigration rates, ms of other solutions are
used to probabilistically select which solutions among the pop-
ulation setwillmigrate. Themain difference between recombination基于生物地理学（BBO）算法下的管壳式换热器的
设计和经济最优化
摘要：管壳式换热器的成本最小化是一个关键课题。传统的设计方法，除了浪费时间，不能保证经济最优化方案的实现。因此，在这个研究中，一种基于生物地理学最优化算法的新型管壳式换热器优化设计方法产生了。BBO算法与其他演化的算法相比，在达到全球最低（成本）方面有一些优势。在这项研究中，BBO技术被运用到使设备总成本最低化里，其中包括资本投资，和有关于通过改变一系列设计变量例如管长，管子外径，间距大小，折流板间距等的管壳式换热器的抽吸的，打折扣的每年的能源费用总和。以这种被提到的方法为依据，一种全新的计算机代码被应用于管壳式换热器的最优化设计，而且3种不同的测试例子证明了这种算法的有效性和准确性。最后，那些结果和通过文学方式到现在的情况进行对比。得到的结果表明BBO算法可以成功用于管壳式换热器的最优化设计。关键词：管壳式换热器；最优设计；经济最佳化；基于生物地理学的最优设计