Abstract
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.
Highlights
Nowadays several algorithms are developed that are inspired by the nature
One of the most successful foragers is E. coli bacteria, which use chemical sensing organs to detect the concentration of nutritive and noxious substances in its environment
We present some modifications for the bacterial foraging optimization algorithm (BFOA) by adapting the crossover operator used in genetic algorithm (GA)
Summary
Nowadays several algorithms are developed that are inspired by the nature. The main principle behind the nature-inspired algorithm is interpreted as the capacity of an individual to obtain sufficient energy source in the least amount of time. Based on the E. coli foraging strategy, Passino proposed bacterial foraging optimization algorithm (BFOA) [2,3,4] which maximizes the energy intake per unit time. We present some modifications for the BFOA by adapting the crossover operator used in GA. In BFOA, 50 percent of bacteria are replicated at the same location and start searching from the same location. As a result they miss some useful parameters in the search space. This has motivated us to investigate crossover BFOA, which can find global optimal solution more effectively.
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