In this study, we have suggested a meta-heuristic algorithm based on swarm intelligence for single-objective optimization problems. Our inspiration comes from historical and behavioral fashions of pirates at 1600s and 1700s which also is known as the golden age of piracy. They navigate in the oceans to find treasures and we search the problem space to find a better solution. Triangle trade is the search space and each pirate ship is a search agent. A bunch of well-known benchmark functions are used to run some qualitative, quantitative and statistically tests to see the outcome of the suggested algorithm. Obtained results shows that the algorithm is able to explore and exploit in search space as expected and also has the ability of improving the average fitness of population and improving the best so far solution. At the end; results of suggested algorithm comparing to other well-known ones such as Particle Swarm Optimization, Gravity Search Algorithm, Ranking Genetic Algorithm and Sparrow search algorithm shows that it is able to outperform these algorithms in most of the studied cases.