# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 4 Original and Hybrid Whale Optimization Algorithm
### Deficiences of the Original WOA Approach and Related Literature  
We performed practical simulations with the original WOA implementation and concluded that some possibilities for improvement exist. We tested WOA on a standard unconstrained (bound-constrained) benchmark set and observed two deficiencies: inappropriate exploitation-exploration balance adjustment and the premature convergence.  
The process of exploration in the original WOA is performed only in case when both conditions \(p<0.5\) and \(|A\geq 1|\) are satisfied. Moreover, the exploration directs the search process towards existing random solutions from the population and then performs search in its neighborhood (refer to Equation (31). Novel random solutions from the search domain are not considered.  
In all other cases, the search process of the original WOA conducts intensification by generating new solutions in the direction of the current best solution vector \(\vec{X}^{*}\). If the conditions \(p<0.5\) and \(|A<1|\) are satisfied, the search process is performed by utilizing Equations (24)-(27), and when the condition \(p>=0.5\) holds, the Equation (29) is used.  
The WOA's behavior described above exhibits the inappropriate exploitation-exploration balance adjustment that has influence on the algorithm's behavior, especially in the early iterations of its execution. In the early phases of algorithm's execution, the exploration power in some runs is not enough for the WOA to find the right part of the search space, where an optimum solution is located. This as a consequence generates worse mean values. Moreover, the exploration is oriented towards existing random solutions from the population. Completely new solutions are not generated at all.  
According to our conducted simulations, the exploitation-exploration trade-off, that is set towards (in favor of) exploitation may lead to the scenario of premature convergence, when the search process gets trapped in some of the suboptimal regions of the search domain. Premature convergence takes place when two inner control parameters \(\vec{A}\) and \(\vec{C}\) are unable to generate better solutions in consecutive iterations, and as a consequence, the diversity of population is lost. Moreover, due to the premature convergence, convergence speed of the WOA is not satisfying in some runs.  
For example, in some algorithm runs, due to randomization, the initial population is generated far from the optimal domain of the search space. By utilizing exploration, which examines the neighborhood for such solutions, the search process is unable to converge to the optimal region. In such cases, solutions can not be improved in consecutive generations, and the whole population loses diversity.  
Some research that proposed improved/hybridized versions of the WOA, which overcome some of its deficiencies, already exists in the literature. For example, [PERSON] and [PERSON] presented two hybridized WOA metaheurisitcs, where the simulated annealing (SA) was incorporated into the WOA to improve and to confirm the global best solution found by the WOA search process [23]. In the LWOA (Levy fight trajectory-based whale optimization algorithm) metaheuristics, after the solutions are updated by the WOA, another update is performed by utilizing the Levy fight trajectory [24]. In [83], enhanced WOA (EWOA) for tackling skeletal structure problems was proposed. The EWOA in each iteration updates only selected variables of the chosen candidate solution.  
The improved WOA (IWOA), that adopts a differential evolution (DE) mutation operator and utilizes adaptive strategy for balancing between exploitation and exploration was proposed in [25] and proved to be more efficient an approach than the original WOA. Another improved WOA version that is based on different searching paths and perceptual disturbance was proposed and tested on 23 standard unconstrained benchmarks and obtained better result quality than the original version [84].