Backtracking biogeographybased optimization for n

Authors: Weian Guo Ming Chen Lei Wang Qidi Wu

Publish Date: 2015/12/14

Volume: 44, Issue: 4, Pages: 894-903

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Abstract

As a novel Evolutionary Algorithm EA BiogeographyBased Optimization BBO inspired by the science of biogeography draws much attention due to its significant performance in both numerical simulations and practical applications In BBO the features in poor solutions have a large probability to be replaced by the features in good solutions The replacement operator is termed migration However the replacement causes a loss of the features in poor solutions breaks the diversity of population and may lead to a local optimal solution To overcome this we design a novel migration operator to propose Backtracking BBO BBBO In BBBO besides the regular population an external population is employed to record historical individuals The size of external population is the same as the size of regular population The external population and regular population are used together to generate the next population After that the individuals in external population are randomly selected to be updated by the individuals in current population In this way the external population in BBBO can be considered as a memory to take part in the evolutionary process The memory takes into account both current and historical data to generate next population which enhances algorithm’s ability in exploring searching space In numerical simulation 14 classical benchmarks are employed to test BBBO’s performance and several classical nature inspired algorithms are use in comparison The results show that the strategy in BBBO is feasible and very effective to enhance algorithm’s performance In addition we apply BBBO to mechanical design problems which involve constraints in optimization The comparison results also exhibit that BBBO is very competitive in solving practical optimization problemsThis work was sponsored by the National Natural Science Foundation of China under Grant No 61503287 No 71371142 and No 61203250 Program for Young Excellent Talents in Tongji University 2014KJ046 Program for New Century Excellent Talents in University of Ministry of Education of China PhDPrograms Foundation of Ministry of Education of China 20100072110038

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