Improved symbiotic organisms search algorithm for solving unconstrained function optimization

Abstract

Recently, Symbiotic Organisms Search (SOS) algorithm is being used for solving complex problems of optimization. This paper proposes an Improved Symbiotic Organisms Search (I-SOS) algorithm for solving different complex unconstrained global optimization problems. In the improved algorithm, a random weighted reflective parameter and predation phase are suggested to enhance the performance of the algorithm. The performances of this algorithm are compared with the other state-of-the-art algorithms. The parametric study of the common control parameter has also been performed.

Keywords

• Symbiotic organisms search
• Population based algorithm
• Unconstrained optimization
• Random weighted difference vector
• Random weighed reflection