 bacterial foraging optimization algorithms. Another class of algorithms and these class of algorithms are being inspired by the foraging behavior of the bacteria in nature. These algorithms are being proposed by Kevin Pethino in 2002. So again they are very neighbor and youngest versions of the evolutionary algorithms inspired from the foraging behavior of bacteria. These algorithms are specially designed by inspired from specific types of bacteria including the ischirisha coli and some other bacteria. Specifically these algorithms are been inspired by a very specific and special property of the bacteria which is called as the chemotaxis. In natural systems bacteria has the ability of chemotaxis. In chemotaxis basically what the bacteria do this is this property is related to some chemicals in the environment of the bacteria. So bacteria have the potential to move towards or move away from that chemical in their environment in which they are living. Depending upon the type of that chemical and the intensity of that chemical so bacteria will either move toward that chemical or will move away from that chemical. So this potential there they consist and this potential of the bacteria the natural systems being exploited in these bacterial foraging optimization algorithms. Bacteria basically they perceive the direction of food they perceive the direction to food based on the gradients of chemicals in their environment. Another ability again we can we can say that the food may also be considered as a type of chemical in the environment in this specific scenario. So this is the gradient of those chemicals which are perceived by the type of bacteria in their environment and now the bacteria will logically decide whether to go towards the food or not to go toward that food. So my decision strategy is over there. In this module basically we are discussing the inspiration the metaphor and the strategy of the bacterial foraging optimization algorithms that how they were inspired what inspiration make them possible what is the metaphor they are using for their algorithm designing and what strategy they are using during their algorithm run. So in the metaphor we say that the food gradient to a system environment that can be pursued by different types of bacteria differentially. Similarly bacteria are able to secrete attract or repel chemicals into the environment and similarly they also can perceive each other in on the same way due to the gradients of chemicals in the environment that they can perceive each other and they can perceive the chemicals or the availability of food in the environment by their specialized mechanisms. So if they decide to move to order to move away from that specific type of chemical bacteria majority of the bacteria not all bacteria in nature but different types of bacteria there which have the potential of motion which we called locomotion and they have some specialized types of propellers or systems were there for their locomotion in different bacterias there are locomotory systems called as the flagella so they are they are using these flagella and through moving these flagella they are able to move away or move toward that specific type of chemical or to that gradient basically. Now it has also been observed that bacteria sometimes they move randomly aware towards that specific signal but sometimes they are they also show a behavior in which they they can move in a systematic manner but whatever they are doing this moving ability will be called here as a swimming. In our algorithms may this movement terminology go its behavior go swimming can I'm say I'm happy terminology use karenge so depending upon the cell cell interaction these these bacterias are able to form a swarm to form a group for the for the food source so again they are doing something in grouping so that's why again it's a it's a type of a swarm-based algorithms. Now where there are group-based algorithms where there are integrations there are emergent behavior arise and that's emergent behavior is basically providing the capability to solve the complex problem and to give the global optimum. Now what strategy these bacterial foraging optimization algorithms are using so the strategy of the algorithm is basically based upon the natural behavior of the bacteria which we have discussed in the metaphor. So but that whole strategy of the algorithm is being achieved in a series of three basic processes. This is chemo-taxes and the process of chemo-taxes is the major work of the algorithm. The next step is just the rest of the part of the algorithm and the system goes towards optimization. So major work here and here the cost of the cells are basically they are derated by the proximity to the cells and cells basically move to the cost surface area but the movement is one by one basically. So this is step number one the process number one of the algorithm, the strategy. The second algorithm is used in the process number two is the reproduction use and the reproduction may only those cells that are performed well over their generations and they survive in the population and they will be retained by the system in the next generation as well. The third series process is elimination and dispersal basically. And in this step we are cells are being discarded. The cells which are not fitter, which are less fitter, which are not selected, they are discarded in this step and they are eliminated and in their place randomly new cells are being inserted, new agents are being inserted with low probability. Here it is also that the probability rate of discarding elimination is low here. So that's why the rate of insertion is also probability low. So the whole algorithm is using a specific strategy in these three basic, in a series of three basic processes which we discussed. Number one, the chemotaxis, number two, the reproduction and number three, the elimination or the discard step of the algorithm.