Abstract
The successful implementation of particle swarm optimization (PSO) for solving portfolio optimization problems is widely documented. However, its execution is restricted within a single-objective optimization framework. The challenge of utilizing PSO based upon a multi-objective optimization framework is identifying the global best solution since a set of compromising solutions is obtained rather than a single best solution. The majority of the multi-objective PSO (MOPSO) proposed in the literature employs the Pareto dominance relation for updating solutions and repository. By using this method, unfortunately, performance of MOPSO deteriorates if the number of optimized objective increases because the chance that solutions do not dominate each other rises. To overcome this problem, the winning score assignment method is developed by taking into account the interacting relations between optimized objectives during fitness assignment process. The proposed method is integrated into the MOPSO and the resulting algorithm is named as the “winning score MOPSO” denoted by WMOPSO. The WMOPSO is experimented for solving portfolio optimization problems containing up to four optimized objectives. The performance of WMOPSO is benchmarked with its original version based upon four standard comparison criteria. Regardless of performance criteria, the comparison results reveal that WMOPSO outperforms MOPSO. In addition, its superiority is more pronounced for the many-objective optimization problems.
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- 1.
For 100 solution, all pair of solutions equal to C(100, 2) = (100!)/(98!2!) = 4,950.
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Suksonghong, K., Boonlong, K. (2017). Particle Swarm Optimization with Winning Score Assignment for Multi-objective Portfolio Optimization. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_83
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