A novel many-objective symbiotic organism search algorithm for industrial engineering problems

The focus of multi-objective optimization is to derive a set of optimal solutions in scenarios with multiple and often conflicting objectives. However, the ability of multi-objective evolutionary algorithms in approaching the Pareto front and sustaining diversity within the population tends to diminish as the number of objectives grows. To tackle this challenge, this research introduces a novel Many-Objective Symbiotic Organism Search (MaOSOS) for many-objective optimization. In this method the concept of reference point, niche preservation and information feedback mechanism (IFM) are incorporated. Niche preservation aims to enhance selection pressure while preserving diversity by splitting the objective space. Reference point adaptation strategy effectively accommodates various Pareto front models to improve convergence. The IFM mechanism augments the likelihood of selecting parent solutions that exhibit both strong convergence and diversity. The efficacy of MaOSOS was validated through WFG1-WFG9 benchmark problems (with varied number of objectives ranging from 5 to 7) and five real-world engineering problems. Several metrics like GD, IGD, SP, SD, HV and RT metrics were used to assess the MaOSOS's efficacy. The extensive experiments establish the superior performance of MaOSOS in managing many-objective optimization tasks compared to MaOGBO, MaOJAYA, MaOTLBO and MaOSCA.