  This article introduces an enhanced particle swarm optimizer (PSO), termed
Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal
array-based learning approach to cultivate an improved initial swarm for PSO,
significantly boosting the adaptability of swarm-based optimization algorithms.
The article further presents archive-based self-adaptive learning strategies,
dividing the population into regular and elite subgroups. Each subgroup employs
distinct learning mechanisms. The regular group utilizes efficient learning
schemes derived from three unique archives, which categorize individuals based
on their quality levels. Additionally, a mutation strategy is implemented to
update the positions of elite individuals. Comparative studies are conducted to
assess the effectiveness of these learning strategies in OPSO-m, evaluating its
optimization capacity through exploration-exploitation dynamics and population
diversity analysis. The proposed OPSO-m model is tested on real-parameter
challenges from the CEC 2017 suite in 10, 30, 50, and 100-dimensional search
spaces, with its results compared to contemporary state-of-the-art algorithms
using a sensitivity metric. OPSO-m exhibits distinguished performance in the
precision of solutions, rapidity of convergence, efficiency in search, and
robust stability, thus highlighting its superior aptitude for resolving
intricate optimization issues.
