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| Hi-index | 12.05 |
Motivated by the compensatory property of EA and PSO, where the latter can enhance solutions generated from the evolutionary operations by exploiting their individual memory and social knowledge of the swarm, this paper examines the implementation of PSO as a local optimizer for fine tuning in evolutionary search. The proposed approach is evaluated on applications from the field of computational finance, namely portfolio optimization and time series forecasting. Exploiting the structural similarity between these two problems and the non-linear fractional knapsack problem, an instance of the latter is generalized and implemented as the preliminary test platform for the proposed EA-PSO hybrid model. The experimental results demonstrate the positive effects of this memetic synergy and reveal general design guidelines for the implementation of PSO as a local optimizer. Algorithmic performance improvements are similarly evident when extending to the real-world optimization problems under the appropriate integration of PSO with EA.