Poor and rich optimization algorithm: A new human-based and multi populations algorithm - Samareh Moosavi, Seyyed Hamid; Bardsiri, Vahid Khatibi (Engineering applications of artificial intelligence, 2019)
This paper presents a new optimization algorithm called poor and rich optimization (PRO). This algorithm is inspired by the efforts of the two groups of the poor and the rich to achieve wealth and improve their economic situation. The rich always try to increase their class gap with the poor by gaining wealth from different ways. The rich are always trying to increase their class gap with the poor by acquiring wealth from different ways. On the other hand, the poor try to gain wealth and reduce their class gap with the rich. On the other hand, the poor try to gain wealth and reduce their class gap by modeling the rich. This struggle is always going on and should be mention that the poor may get rich and vice versa. The proposed algorithm is evaluated using 33 test functions and the simulation results are compared with a number of new and well-known optimization algorithms. The evaluation domain includes uni-modal, multi-modal, fixed dimension, hybrid and large scale functions. In addition, for more precise evaluation, Tension/compression spring design, pressure vessel design, Gear drain design, and three-bar truss design problems are solved by PRO algorithm. PRO algorithm has had better performance in these four problems by finding optimal values of parameters as compared to other algorithms. Finally, PRO algorithm was used to estimate software effort by UCP for more accurate evaluation. The obtained results confirmed the superiority of PRO in exploration, exploitation and convergence aspects, compared to other algorithms.