*3.2.3 Demerits of GBC*

Inspite of mentioned advantage GBC has few problems also. GBC are slow convergent speed in large computations and accuracy is less. GBC may face premature convergence problem for more duration application. The population size is fixed, and size is variations or non-autonomous. Individuals can extend the searching space and

increase the probability of finding global optimization solution; however, it costs much time in each generation; oppositely, it may obtain a local minimum.

#### *3.2.4 Applications of artificial bee colony (ABC) algorithm*

GBC are more affine towards single-objective numerical optimization problems but not limited and can be extended to multi-parametric. Decision making, time schedule, assignment, search, inception, boundary setting, and network issued are other more applied fields of GBC [21]. GBC is capable to handle constrained and unconstrained, continuous and discrete, differential and non-differential oriented problems [22]. GBC is not specific in domain, applicable from agriculture to industry, and rural area to military field.

#### **3.3 Fish swarm algorithm (FSA)**

Fish swarm algorithm inspired by the behaviour of movement of aquatic fish in liquid medium. The target is picked randomly and moves toward in an iterative manner. Visually shorter distance considered an initial step, influences on final step. Initial values remain constant and considered along parameters. Suitable initial value selection leads toward the best optimum solution. Fishes are capable of venturing into bigger steps in search of a larger environment where they exist. So, fish is capable of escaping from unfavourable circumstances at any stage. But some deficiencies in large values may cause low steadiness. Global search is potential factor in generating local search with a larger visual position of FSA. Better fitness can be found for better fitness to search for parameters to make the algorithm steady and accurate. Fish are capable of moving quickly towards the target and can get passed from local best search results. FSA algorithm design has undergone many changes in design in order to fulfil the needs of different types of problems. The variation in algorithm can be grouped into solutions of FSA for continuous and discrete, combinatorial and binary, multi-parametric and hybrid FSA. Fei et al. [23] selected nine search positions to initialize the Afs for motion estimation. Zhu et al. [24] and Gao et al. [25] used the chaotic transformation [26] method to generate a more stable and uniform population. Kang et al. [27] used a uniform initialization method to initialize the population, while Liu et al. [28] initialized the

Afs based on the optimization problem in hand. The MSAFSA [29] model introduced both the leaping and swallow behaviors to escape from the local optima and reduce, Yazdani et al. [30, 31] introduced mNAFSA for optimization in dynamic environments.

#### *3.3.1 Concept*

Fish Swarm algorithm and background is discussed. Notations used are X, V, S, Xv indicate current position of fish, distance, step, position, respectively. N visual fishes are indicated as X1, X2, X3 … … .. Xn. Y = f(X) denotes the food concentration of the AF at the current position, di;j =||Xi – Xj ||. The FSM involves four key operations: preying behaviour, swarming behaviour, following behaviour and random behaviour. Preying is fish behaviour to move itself towards high concentration of food. It is represented mathematically as in Eq. (11) considering with in visual distance ith fish. The fish preying will continue to try number of times if not satisfied within, then

*Bio-inspired Optimization: Algorithm, Analysis and Scope of Application DOI: http://dx.doi.org/10.5772/intechopen.106014*

randomly computed using Eq. (12). Fishes group among themselves to from any danger situations against them. Mathematically, central position in fish swarm is computed as in Eq. (13). Fish when a locates good concentration of food. The preying movement for fish in step movement is represented in Eq. (14). Few fishes randomly move freely if lie in sparely concentrated food. This behaviour is modeled as in Eq. (15) (**Figure 4**).

$$X\_j = X\_i^{(t)} + V \* rand() \tag{11}$$

$$X\_i^{(t+1)} = X\_i^{(t)} + V \* rand()\tag{12}$$

$$\mathbf{x}\_{cd} = \frac{\sum\_{j=1}^{n^f} \mathbf{x}\_{jd}}{n\_f} \tag{13}$$

$$\mathbf{X}\_{i}^{(t+1)} = \mathbf{X}\_{i}^{(t)} + \mathbf{S} \* rand() \* \left(\frac{\mathbf{X}\_{j} - \mathbf{X}\_{i}^{(t)}}{||\mathbf{X}\_{j} - \mathbf{X}\_{i}^{(t)}||(t)||}\right) \tag{14}$$

$$X\_i^{(t+1)} = X\_i^{(t)} + V \* r \text{and}() \tag{15}$$

#### *3.3.2 Algorithm and flowchart*

FSA perform record one if new. This search continues until end is not met following four operational steps as mentioned in previous section. The algorithm FSA is shown in briefed in **Figure 5**.
