Portfolio management, a critical application of financial market analysis, involves optimising asset allocation to maximise returns while minimising risk. This paper addresses the notable research gap in analysing historical financial data for portfolio optimisation purposes. Particularly, this research examines different approaches for handling missing values and volatility, while examining their effects on optimal portfolios. For this portfolio optimisation task, this study employs a metaheuristic approach through the Swarm Intelligence algorithm, particularly Particle Swarm Optimisation and its variants. Additionally, it aims to enhance portfolio diversity for risk minimisation by dynamically clustering and selecting appropriate assets using the proposed strategies. This entire investigation focuses on improving risk-adjusted return metrics, like Sharpe, Adjusted Sharpe, and Sortino ratios, for single-asset-class portfolios over two distinct classes of assets, cryptocurrencies and stocks. Considering relatively high market activity during pre, during and post-pandemic conditions, experiments utilise historical data spanning from 2015 to 2023. The results indicate that Sharpe ratios of portfolios across both asset classes are maximised by employing linear interpolation for missing value imputation and exponential moving average smoothing with a lower smoothing factor (α). Furthermore, incorporating assets from different clusters significantly improves risk-adjusted returns of portfolios compared to when portfolios are restricted to high market capitalisation assets.