3.5.4. Elimination and Dispersal

A sudden change in the environment where the bacteria lives might occur due to various reasons and this phenomenon is called elimination and dispersal. The bacteria may be living at a better nutrient gradient environment, but due to environmental changes, some of the bacteria may ge<sup>t</sup> killed or dispersed to a new location. Due to this, many bacteria are spread all over the environment from the human intestine to hot springs and also to the underground environment. For implementing these phenomena of the bacteria in BFOA, some of the bacteria are randomly liquidated with a much lower probability, whereas the new replacements are initialized over the search space randomly. These events have the possibility of destroying the chemotaxis process, or they may assist the chemotaxis process because the dispersal of the bacterium may place it in a new good food location.

The above explained BFOA finds its application in various fields. In reference [73], a hybrid least square fuzzy-based BFOA is proposed for the harmonic estimation in power system voltage and current waveforms. Due to its capability of handling non-linear optimization, the phase estimation is done by BFOA and the linear least square method is used for amplitude estimation of the harmonic component. In reference [76], the authors have analyzed the chemotaxis process of BFOA from the classical gradient descent point of view. In this method the convergence speed of the BFOA algorithm has been enhanced. BFOA has also been implemented for active noise cancellation systems successfully [77]. Authors in reference [78] presented a grid-tied PV system based on an active shunt power filter (ASPF) technique. As controlling a dc-link voltage using PI controller is difficult due to the existence of varying loads, in this paper BFOA is used to optimize the PI controller parameters. A PSO-guided BFOA algorithm is considered for PV parameter estimation in reference [79]. Here, the optimization problem is solved using PSO, BFOA, and PSO-guided BFOA in an LDK PV test module and it is found that the later provides best fitness value. In [80], both conventional and computational techniques with hybridization of the algorithms are used for maximum PV power extraction and the performance of the algorithms is compared. Here P&O, fuzzy-based P&O, and

fuzzy P&O with parameters tuned by BPSO (i.e., BFOA-PSO) have been considered for PV systems, among which, the later BPSO tuned fuzzy P&O was found to be the best one. BFOA has been used as an efficient parameter extraction technique for PV cells. It shows more accurate results compared to the classical Newton–Raphson method, PSO, and enhanced simulated annealing for different PV modules with different test conditions [81]. From the literature, it is seen that BFOA can be applied to various global search problems for finding out the best solution.

### **4. Critical Evaluation of MPPT Algorithms**

While selecting an algorithm for optimization problem, various aspects need to be considered and those are reliability, implementation cost, convergence speed, complexity of the algorithm, etc. The evolutionary algorithms play an important role while considering the partial shading condition of PV panels. From the literature, it is seen that there are many MPPT techniques available with different control techniques, and there is still a lot to explore. The deep analysis of the algorithms gives clear knowledge about the recent advancement in the said area. It shows the various factors affecting achieving the optimization goal and also shows the limitations. Among the five important MPPT algorithms discussed, here PSO is found to be the most used one. Basic PSO has a simple coding structure and is quite effective at tracking GMPP but sometimes due to rapidly changing weather conditions, it may reduce its convergence speed and start oscillating near the GMPP. Hence, in the literature it is found that many researchers' have implemented hybrid PSO or modified PSO to achieve the optimization goal. It is seen that PSO with DE, PSO with P&O, PSO with genetic algorithm, etc., has been used, which gives a better convergence speed and less oscillation. The swarm intelligent algorithms like ACO and ABC involve simple and cost-effective implementation, and perform better than the standard PSO algorithm. However, at some period of time, these fall on local maxima. The performance of those algorithms can be further improved by combining them with conventional, artificial intelligence techniques or using soft computing techniques. This will reduce the convergence time and will track the GMPP. The DE algorithm is quite similar to the swarm intelligent algorithms but in some cases, it fails to track the GMPP as the parameters have no direction. Hence, it may follow a wrong direction. This algorithm can be improved by hybridizing with the soft computing techniques. BFOA based on bacteria foraging behavior provides a large search space and simple calculations, and the limitations of the algorithm can be overcome by modifying the parameter selection process or by combining it with other optimization techniques. The advantages and disadvantages of these five algorithms are listed in Table 1.

In Table 2, the use of nature-inspired algorithms as MPPT techniques for various PV models are analyzed. These techniques can be further improved by narrowing the search space, limiting the number of optimization parameters, and also by selecting suitable control parameters. This, in turn, can increase the speed of convergence and can also find the best fitness value. Both the conventional and soft computing algorithms can be integrated such that the limitations of both the algorithms can be reduced and the resulting hybrid algorithm may improve the performance of the PV system. However, this might increase the implementation cost and complexity of the system. From this review of the literature, it is noted that most of these algorithms are similar and vary with a narrow border. Therefore, selection of the algorithms solely depend on the researcher's optimization criteria, which may be a cost function, a simple and easy to implement technique, etc. Therefore, an efficient, robust, economical, and simple algorithm has to be developed that, in turn, can increase the use of a PV system to its optimum.


**Table 1.** Advantages and disadvantages of reported algorithms.

**Table 2.** Comparison of various algorithms used in the literature.



**Table 2.** *Cont.*
