A Crossover Bacterial Foraging Optimization Algorithm

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. 1. Introduction 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. In the process of foraging, the animals with poor foraging strategies are eliminated, and successful ones tend to propagate [1]. One of the most successful foragers is E. coli bacteria (those living in our intestines), which use chemical sensing organs to detect the concentration of nutritive and noxious substances in its environment. The bacteria then move within the environments via tumble and runs, avoiding the noxious substances and getting closer to food patch areas in a process called chemotaxis. Based on the E. coli foraging strategy, Passino proposed bacterial foraging optimization algorithm (BFOA) [2–4] which maximizes the energy intake per unit time. So as to improve BFOA performance, a large number of modifications have already been undertaken. Some of the modifications are directly based on analysis of the components [5–8] while others are named as hybrid algorithms [9–11]. During the past two decades, the genetic algorithm (GA) has claimed its suitability for dealing with optimization problems by academic and industrial communities. A possible solution to a specific problem is encoded as a chromosome, which consists of a group of genes. Each chromosome refers to a search space and is decided by a fitness evaluation. The GA uses basic genetic operators such as crossover and mutation to produce the genetic composition of a population. The crossover operator produces two offspring by recombining the information of two parents. Randomly gene values are changed using the mutation operator. The