Improved Symbiotic Organism Search (I-SOS) for global numerical optimization

Abstract

The no free lunch theory states that no specific heuristic method can effectively solve all problems. This theory has created opportunities for researchers to improve existing heuristic methods or even discover new approaches. One algorithm that has gained considerable attention from researchers is the Symbiotic Organism Search (SOS) algorithm. Its appeal lies in its simplicity and minimal parameter requirements, making it applicable to various problem domains. However, the SOS algorithm also has its limitations. This study focuses on the enhancement of SOS by introducing a modified random weight (MRW) method during the parasitism phase, resulting in the Improved SOS (I-SOS) algorithm. The effectiveness of this algorithm is tested in solving unconstrained problems using 26 benchmark functions and compared to several existing heuristic methods in the literature. The simulation results show that I-SOS outperforms basic SOS as well as several other algorithms.

Keywords

• benchmark
• enhancement
• heuristic
• modified random weight
• no freelunch