###### Abstract  
Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators. metaheuristic search algorithm; swarm intelligence; random search; multi-agent systems; optimization; behavior; pollination of cocoa flowers 202129  
only method to ensure successful fertilization because self-pollination is not suitable for cacao trees and will not result in successful fertilization [2]. This situation implies matching behaviors of the pollinators. We aim to investigate this process by pollination simulations on cocoa plants by using three methods of pollinator collaboration:  
* Based on Swarm Intelligence Algorithms;
* Based on Individual Random Search;
* Based on Multi-Agent Systems differential search methods. The study of optimization algorithm behavior to tackle pressing real-world problems has recently attracted many researchers' attention. Currently, new algorithms are set up and focused on achieving a pre-set desired optimization goal. While this can be useful and efficient in the short term, it is insufficient in the long run as it needs to be repeated for any new problem that occurs under potentially new specific difficulties. Therefore, one algorithm cannot be used for all real-world issues. The development of optimization algorithms is essential because optimization problems can occur in various scientific fields such as economics, engineering, and medicine. There are still many researchers around the world working to solve problems in this field. Classical optimization algorithms are not very efficient at solving real-world problems because they cannot find the global optima or it requires massive efforts. Bn contrast, metaheuristic algorithms are more robust at avoiding local optima. They do not need a cost function gradient because the algorithm's main \"trick\" is also to exploit randomness and concurrency [3]. The paper focuses on theoretical and empirical research investigating approaches needed to analyze stochastic optimization algorithms and performance assessment concerning different criteria. Figure 1 shows the FP process to pollinate cocoa plants. The FP can use different search methods to ensure that the FP can collect enough nectar. However, if the tree pollination has occurred, the tree will no longer expose flowers and not produce nectar. Consequently, FP will have to seek out other trees that have not yet pollinated to obtain more nectar.