An ensemble symbiosis organisms search algorithm and its application to real world problems

Abstract

In this study, an ensemble algorithm has been proposed, called Quasi-Oppositional Symbiosis Organisms Search (QOSOS) algorithms, by incorporating the quasi-oppositional based learning (QOBL) strategy into the newly proposed Symbiosis Organisms Search (SOS) algorithm for solving unconstrained global optimization problems. The QOBL is incorporated into the basic SOS algorithm due to the balance of the exploration capability of QOBL and the exploitation potential of SOS algorithm. To validate the efficiency and robustness of the proposed Quasi-Oppositional Symbiosis Organisms Search (QOSOS) algorithms, it is applied to solve unconstrained global optimization problems. Also, the proposed QOSOS algorithm is applied to solve two real world global optimization problems. One is gas transmission compressor design optimization problem and another is optimal capacity of the gas production facilities optimization problem. The performance of the QOSOS algorithm is extensively evaluated and compares favorably with many progressive algorithms.