This paper introduces a novel Nature-inspired Multi-Objective Particle Swarm Optimization algorithm for Multifactorial Problems, called MOPSO-mfact. The MOPSO-mfact algorithm operates in two processing steps: an initial pre-search in a unified search space, followed using a dominance operator to identify the best skill factor. Subsequently, individuals with identical skill factors are grouped into sub-swarms for multitasking optimization, avoiding random mating probabilities. MOPSO-mfact was evaluated using 36 Multifactorial (ETMOF) benchmark suite. Comparative analysis using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics, along with the Mean Standard Score (MSS), determined the best multitasking approach. Parameter optimization was achieved via sensitivity analysis with the Taguchi method. MOPSO-mfact demonstrated promising results, achieving a strong MSS for 28 ETMOF problems and solving 33 out of 36 problems based on MIGD metrics.

This paper presents a new Multi-Objective Particle Swarm Optimization algorithm for Multifactorial Optimization Problems referred to as MOPSO-mfact. It operates in two distinct steps with two processing steps. Initially, a pre-search is conducted in a unified search space where all individuals are optimized to address various tasks simultaneously. Then, the dominance operator is used to explore the solution encoding search space and identify the best skill factor. In the subsequent step, individuals with identical solution skill factors are grouped together to create multiple sub-swarms for multitasking optimization. This approach leverages the dominance operator instead of relying on random mating probabilities. The MOPSO-mfact algorithm was tested on a set of Multifactorial test benchmarks called “ETMOF” which includes 36 multi/many objectives optimization problems. A comparative study is conducted toward the Inverted General Distance (IGD) and the Mean Inverted General Distance (MIGD) metrics. The Mean Standard Score (MSS) is used to determine the best approach for multitasking optimization. Parameter optimization for MOPSO-mfact is achieved through sensitivity analysis using the Taguchi method, ensuring optimal performance. The MOPSO-mfact algorithm demonstrated promising results, achieving a good MSS result for solving the 28/26 ETMOF test suite as assessed by the IGD indicators. Additionally, it performed well, solving 33 out of 36 problems in the ETMOF suite when evaluated using the MIGD metric.