Set-based Particle Swarm Optimization for Portfolio Optimization with Adaptive Coordinate Descent Weight Optimization

Set-based algorithms have been shown to successfully find optimal solutions to the portfolio optimization problem and to scale well to larger portfolio optimization problems. Set-based algorithms work by selecting a sub-set of assets from the set universe. These assets then form a new search space where the asset weights are optimized. Erwin and Engelbrecht purposed such an algorithm that was shown to perform similarly to a well known genetic algorithm for portfolio optimization. The proposed algorithm, set-based particle swarm optimization (SBPSO), used a meta-huerstic for the weight optimization process - unlike previous set-based approaches to portfolio optimization. Erwin and Engelbrecht also developed several modifications to SBPSO that improved its performance for portfolio optimization. This paper investgates an alternative weight optimizer for SBPSO for portfolio optimization, namely adaptive coordinate descent (ACD). ACD is a completely deterministic approach and thus ensures that, after a finite time, an approximation of a global optimum will be found. It is shown that SBPSO for portfolio optimization using ACD for weight optimization found higher quality solutions than the current SBPSO algorithm, albeit slightly slower.