<![CDATA[The growing emphasis on sustainability in investment decisions necessitates portfolio optimization models that incorporate practical constraints, particularly asset cardinality. This study applies a Cardinality-Constrained Mean–Variance (CCMV) framework, which increases computational complexity due to the limited number of selected assets. To address this, two metaheuristic algorithms, namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are implemented. Using data from 25 stocks observed between March and August 2025, the analysis includes asset screening based on the risk-free rate, portfolio optimization, and performance evaluation using expected return, risk, and the Sharpe ratio. PSO produces an optimal portfolio of 8 assets with a Sharpe ratio of 27.594%, while ABC selects 9 assets and achieves a slightly higher Sharpe ratio of 27.599%. Although the difference is marginal, ABC demonstrates superior computational efficiency. The findings highlight the effectiveness of metaheuristic approaches, particularly ABC, in solving constrained portfolio optimization problems.]]>
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