## 3 Material and Methods
### Methodology
#### 3.2.2 Individual Random Search
We can observe that the three types of algorithms refer to cases of more general random search strategies:

* Individual strategies, such as Levy flight, are such that each FP pursues its trajectory independent of the other FP. The case is most supported also by biological insights into insects flight patterns. We can testify that it is a good method but not the best quality method. * The FP takes a reference to one or more other FP positions for deciding on the next move. This is basically implemented in all Swarm Intelligence algorithms. However, we can see that there is neither a significant advantage concerning nectar intake nor any gain in the number of pollinated trees. From a biological point of view, it also rests on the assumption that insects can recognize their species among other objects in the environment. * A differential approach in which the FP decides the next step based on a reference to the offset between two objects in the environment. Our analysis clearly shows the advantage of this strategy: pollinating the most significant number of trees and including the fairest distribution of nectar within the population. Moreover, the pun is on \"different objects\" and not necessarily other FP to take as reference. It means that the same method might work as well, e.g., other insect species as reference points. This method is subject to further investigations. Practically, the promotion of differential strategies can promote pollination, which would require related farm experiments. The other impact is on the study of related optimization problems from this class of deferred fitness problems. In fact, we can find numerous problems that have been hard to approach so far: the evolution of parasitism as an example from biology. Moreover, there are congruent constraints in the food supply chain, for example, producing farm goods to arrive in a new state at some consumer site. ## 6 Conclusions

Here, we studied the problem of Pollination optimization that came out to be a concurrent optimization problem with a deferred fitness evaluation. FP and trees act together in a seamless but also contingent way to achieve this objective. We investigated three different random search methods: fitness-free versions of Swarm Intelligence Algorithm, i.e., Idle-Jaya, Idle-CSA, and Idle-Cuckoo Search; the Individual Random Search method, i.e., Levy flight; and finally, the Multi-Agent Systems search method, i.e., Defender-Aggressor-Game (DAG). Those methods have been compared for simulating cocoa flower pollination. We chose cocoa pollination as the simulation case because the flowers of the cocoa plant are unique from other plants. They are small (maximum diameter of 3 cm), allowing only small insects to pollinate them. Cross-pollination is the only method to ensure successful fertilization because the self-pollination of unsuitable varieties will not result in successful fertilization. From the results of this study, we can observe the differences among random search strategies. Concerning the main objective and the ratio of pollinated trees, there is an apparent gain from the differential method DAG. There are no significant differences relative to nectar intake and distribution, while there is a better value tendency of DAG. Generally, we can conclude that there is no best method at all points. However, we can refer to the results of this simulation for multi-agent and random searches, which may be more suitable for cocoa pollination in the real world. The simulation approach is also expected to assist a farmer when it comes to the inadequate pollination of cocoa flowers. This can improve the cocoa plant's productivity, as the related setup and experiments can also be performed by farmers using the same simulation software to learn about the influence of the various factors in pollination. By utilizing this simulation, farmers can use this simulation to manage or design their tree or plant placement and the order of potential nests or breeding site for pollinator nests. Conceptualization, W.A.S. and M.K. ; methodology, M.K.