A Crossover Bacterial Foraging Optimization Algorithm | Zendy

Rutuparna Panda | Zendy; Manoj Kumar Naik | Zendy

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A Crossover Bacterial Foraging Optimization Algorithm

Author(s) -

Rutuparna Panda,

Manoj Kumar Naik

Publication year - 2012

Publication title -

applied computational intelligence and soft computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.371

H-Index - 10

eISSN - 1687-9732

pISSN - 1687-9724

DOI - 10.1155/2012/907853

Subject(s) - crossover , foraging , benchmark (surveying) , computer science , mathematical optimization , genetic algorithm , optimization algorithm , algorithm , artificial intelligence , mathematics , machine learning , biology , ecology , geodesy , geography

This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossover mechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not desirable. Seven different benchmark functions are considered for performance evaluation. Also, comparison with the results of previous methods is presented to reveal the effectiveness of the proposed algorithm

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