2. Basic Whale algorithm

1, inspired

Whale optimization Algorithm (WOA) is a new swarm intelligence optimization algorithm proposed by Mirjalili et al from Griffith University in Australia in 2016. Its advantages lie in simple operation, few parameters and strong ability to jump out of local optimum.

Figure 1 Hunting feeding behavior of humpback whales

Humpback whales recognize the location of prey and circle it. Since the position of the optimal position in the search space is unknown, the WOA algorithm assumes that the current best candidate solution is the target prey or close to the optimal solution. After the best candidate solution is defined, the other candidate locations attempt to move to the best and update their positions. This line is represented by the following equation:

3. Hunting behavior

According to the hunting behavior of humpback whales, it swims toward prey in spiral motion, so the mathematical model of hunting behavior is as follows:

4. Hunt for prey

The mathematical model is as follows:

(1) Initialization of qubit Bloch sphere

Second, the algorithm process

The algorithm flow is shown in Figure 1.

FIG. 1 IWOA coverage optimization flowchart

In order to verify the coverage optimization performance of the improved whale algorithm applied to WSN in this paper, MATLAB was used for simulation, and the coverage effects of the original WOA, the improved IWOA and other literatures were compared. The parameters of the experiment were set the same as the corresponding parameters of other literatures. The algorithms involved in comparison are shown in Table 1.

Table 1 Comparison algorithm

The experimental parameters are set the same as those in literature [2], and the monitoring area is set as a two-dimensional plane of 50m×50m ×50 m50m×50m, the number of sensor nodes N=35N= 35N=35, and the sensing radius is R S =5m R_s =5 mRs=5m. Communication radius R C =10m R_c= 10 mRc=10m, 1000 iterations. Initial deployment, FA optimization coverage, and IWOA optimization coverage are shown in Figure 2-4.

Figure 2 Initial deployment

FIG. 3 FA optimization coverage

FIG. 4 IWOA optimized coverage

A comparison of the two is shown in Figure 5.

Figure 5 FAvsIWOA

The experimental parameters are set the same as those in literature [3], that is, the monitoring area is a two-dimensional square plane of 100m×100m ×100 m100m×100m, and the number of isomorphic sensor nodes is deployed N=50N= 50N=50. The sensing radius is R S =10m R_s=10 mRs=10m, the communication radius is R C =20m R_c=20 mRc=20m, and the number of iterations is 1000. Figure 6 shows the initial deployment of EABC, Figure 7 shows the optimal coverage of EABC, Figure 8 shows the optimal coverage of IWOA, and Figure 9 shows the comparison between them.

Figure 6 EABC initial deployment

Figure 7. EABC optimization coverage

FIG. 8 IWOA optimized coverage

Figure 9 EABCvsIWOA

Code download or simulation concerns avatars

[1] Song Tingting, ZHANG Damin, Wang Yiru, et al. Coverage optimization of WSN based on improved Whale optimization algorithm [J]. Chinese Journal of Sensors and Transducers, 2020, 033(003):415-422. [2] Beko M . Mobile wireless sensor networks coverage maximization by firefly algorithm[C]// Radioelektronika. IEEE, [3] Yu Wenjie, LI Xunbo, Yang Xing, et al. Application research of Extrapolated Artificial Bee Colony Algorithm in WSN Deployment Optimization [J]. Instrument Technique and Sensor, 2017(6).