A novel improved symbiotic organisms search algorithm. (20th February 2020)

• Record Type: Journal Article
• Authors: Nama, Sukanta; Saha, Apu Kumar; Sharma, Sushmita
• Is Part Of: Computational intelligence. Volume 38:Number 3(2022)
• Page Start: 947
• Page End: 977
• Publication Date: 2020-02-20
• Subjects: CEC2015 -- function optimization -- self‐adaption -- symbiosis organisms search algorithm -- three‐way mutualism phase -- weighted reflection; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12290
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: For last two decades, nature‐inspired metaheuristic algorithms together with their modified, improved, and hybrid versions have been gaining huge popularity in the field of optimization in solving continuous and complex real‐life optimization problems. In this work, a novel improved symbiosis organism search (SOS) algorithm, called self‐adaptive beneficial factor‐based improved SOS (SaISOS, in short) is suggested. The self‐adaptive benefit factors and a modified mutualism phase (called "Three‐way mutualism phase") have been introduced here to upgrade the performance of SOS algorithm. A random weighted reflection coefficient and a new control operator have also been introduced. To validate the proposed algorithm and to compare its performance with other state‐of‐the‐art algorithms, 15 IEEE‐CEC 2015 functions have been employed and the experimental results confirm that SaISOS provides competitive results on most occasions. Also, the proposed algorithm is used to solve five real‐world optimization problems. Considering the average output, it is observed that the proposed method performs significantly better in solving the real‐world problems compared to the alternative state‐of‐the art techniques considered in this work.