Hello, I've written this article for my college magazine and posting it here too

Introduction

The bacterial Forging Algorithm was developed based on Passino's foraging behavior of the E. coli bacteria. What does foraging mean? It is the method of searching for food or other provisions. So, this algorithm is used to explain the foraging nature of bacteria (E. coli) to obtain as much as possible(maximum) energy during the searching process. The inspiration behind this algorithm is nature. This algorithm has gained a lot of attention among engineers, and researchers & is being adapted in many scenarios due to its simplicity and ease of implementation.

BFO algorithm

The BFO consists of three major mechanisms namely chemotaxis, reproduction, and elimination-dispersal

Chemotaxis: This process is described as the movement or the orientation of a micro-organism concerning the chemical concentration of the substance on which movement takes place.

Reproduction: E .coli reproduces by binary fission. During this type of asexual reproduction, the single DNA molecule replicates and both copies attach, at different points, to the cell membrane.

Elimination Dispersal: this is the removal of bacteria due to surrounding environmental conditions and unexpected conditions occurring in the bacterial colony.

The following relations are used to design the bacterial foraging algorithm:

Chemotaxis:

𝛉ⅈ(j+1,k,l)=𝛉i(j,k,l)+C⎛⎝⎜i⎞⎠⎟Δ(ⅈ)ΔT(i)Δ(ⅈ)−−−−−−−−−√ 𝛉ⅈ𝐣+𝟏,𝐤,𝐥=𝛉𝐢𝐣,𝐤,𝐥+𝐂(𝐢)𝚫ⅈ𝚫𝐓𝐢𝚫ⅈ

The motion in the bacteria can be in two ways tumble or run/swim.

The function represents the bacteria present in a location for the jth chemotactic, kth reproduction, and lth elimination dispersion step.

3: Swim and tumble of a bacterium

Reproduction:

∑j=1NcJ(i,j,k,l) ∑𝒋=𝟏𝑵𝒄𝑱(𝒊,𝒋,𝒌,𝒍)

The health condition of the bacteria is determined by the sum of the step fitness during its life gives the maximum step taken in the chemotaxis process. All the bacteria are arranged in descending order of their health status.

Elimination-Dispersion:

The chemotaxis provides the foundation for local search and reproduction and helps in the convergence of the algorithm, but these conditions are not sufficient for global optimization. The process of dispersion occurs after several cycles of reproduction. Later bacteria are chosen on the present probability to be eliminated and move on to the next location.

Flow Chart of BFO algorithm

Step1: Parameter initialization.

n: the dimension of search space.

S: number of the bacterium.

Nc: chemotactic steps

Ns: swim steps

Nre: Reproductive steps

Ped: the probability of elimination

C(i): step size

Step2: Elimination -dispersal loop: l=l+1

Step3: Reproduction loop: k=k+1

Step4: Chemotaxis loop: j=j+1

Take a chemotactic step (i=1,2,3….S)

Compute fitness function and save this value

and save this until we find a better run.

Tumble movement: In random direction [-1,1].

Swimming process.

Go to the next bacterium(i+1): if i! =S process the next bacteria.

Step5: Check if j<chemotactic steps, continue chemotaxis since the life of the bacteria is not over.

Step6: Reproduction process

Step7: Elimination and dispersal.

Step8: check if the elimination-dispersion loop<elimination and dispersal steps, then go to step2, otherwise, STOP and display the result.

Applications

Some of the outstanding applications of bacterial foraging algorithms are:

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm.

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory . PI Controller-Based Switching Reluctance Motor Drives using Smart Bacterial Foraging Algorithm.

Application for training kernel extreme learning machine.

1. Application of Modified Bacterial Foraging optimization algorithm for optimal placement and sizing of Distributed Generation.

Conclusion

BFOA is currently gaining popularity due to its efficacy over other swarm and evolutionary computing algorithms in solving engineering optimization problems. It mimics the individual as well as grouped foraging behavior of E.coli bacteria that live in our intestine. The analysis reveals how the dynamics of reproduction help in avoiding premature convergence. In recent times, a symbiosis of swarm intelligence with other computational intelligence algorithms has opened new avenues for the next generation Bacterial Foraging Optimization Algorithm 53 computing systems. The chapter presents an account of the research efforts aiming at hybridizing BFOA with other popular optimization techniques like PSO, DE, and GA for improved global search and optimization

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