# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 6 Parameter-Setting of the Optimization Technique  
The performance of any optimization algorithm builds upon the involved control parameters. Again, these parameters play a vital role in asserting an accurate solution. In the BESSA technique, the below parameters are considered. The position control parameter is \(\boldsymbol{\zeta}\), and it varies from 1.5 to 2. The corner between the search is determined by parameter \(a\), which takes any value within a range of 5 to 10. The search cycle number is controlled by parameter \(R\), which takes any value between 0.5 and 2. In the swooping stage, the swing intensity of the bald eagle is increased by parameter \(m1\), \(m2\), which takes any value between 1 and 2. After generating an initial population with the BES technique, SA is implemented to reach the best solution. The solution which is a better fit is acceptable by a probability defined as a Boltzmann Probability(p). Here, \(\theta\) determines degree of fit between the generated solution and the best solution. The temperature parameter is explained by T, which decreases in an organized way in accordance to the cooling scheme. The starting temperature is defined as \(2*1N\). Here, N is the total number of attributes in the data set. In the tournament selection stage, the selection pressure is set at 0.5. By nature, a metaheuristic algorithm is stochastic. The metaheuristic algorithm starts with a randomly generated initial population. The algorithm gradually proceeds and reaches the final stage depending on this initial population. Thus, multiple runs must be executed to derive the best, average, and standard values. To apprehend the performance appraisal of a metaheuristic algorithm, it is investigated with a number of benchmark test functions. In this work, the BESSA is tested on 23 benchmark functions [20] which are used by researchers worldwide. These benchmark functions include multimodal functions, fixed dimensional multimodal functions, and unimodal functions. The exploitation capability of a metaheuristic optimization algorithm is investigated by testing it on unimodal functions, as these functions are characterized by possessing unique global optima but no local optima. The multimodal functions and fixed dimensional multimodal functions are used for investigating the exploration capability of a metaheuristic optimization algorithm. The above functions are characterized by possessing unique global optima and many local optima. To determine the performance efficiency of the BESSA technique, a comparison was made with the recently proposed BES [17] and CamWOA techniques [9]. The Hybrid BESSA technique was also compared with the Hybrid Water Cycle--Moth Flame Optimization algorithm (WCMFO) [21], Artificial Bee Colony (ABC) algorithm [22], Cuckoo Search (CS) [23], Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) [24], Moth Flame Optimization (MFO) [25], Gravitational Search Algorithm (GSA) [26], Water Cycle Algorithm (WCA) [27], Dragonfly Algorithm (DA) [28], and Whale Optimization Algorithm (WOA) [29], as described in the literature [9]. The mentioned algorithms' control parameters are detailed in the Appendix A. For all the above analyses, the maximum number of search particles taken and the total iteration counts were limited to 100 and 1000, respectively, which are common parameters for all the algorithms mentioned above. To reach the right conclusion, the number of runs for each algorithm was fixed at 30. The residuum is analyzed in following section. [PERSON] et al. [32] developed a random reselection PSO for the optimal design of solar PV modules. There are different optimization models which have been developed to handle the model parameters of PV solar panels [33; 34; 35; 36; 37; 38; 39; 40; 41], which include the mutation-based PSO, slime mold algorithm, Gorilla Troops optimizer, Harris Hawk technique, gradient-based optimizer, and Coyote optimization technique.