An Improved Brain Storm Optimization for a Hybrid Renewable Energy System

In this paper, an improved brain storm optimization (BSO) algorithm is proposed to solve the optimization problem in a hybrid renewable energy system. The objective of the proposed algorithm is the minimization of the annualized costs of the system (ACS), the loss of power supply probability (LPSP), and the total fuel emissions. In the proposed algorithm, first, the K-Means clustering method is embedded to make the same clusters have similar solutions. Then, the distance of a city block is taken as the distance measure, which makes the solution feasible. Then, to measure the merits and demerits of each individual, the composite index is utilized as the fitness value. In addition, to improve the efficiency of the algorithm, a pair of crossover and mutation strategies are designed in detail. Finally, a set of realistic instances are used to test the performance of the proposed algorithm, and after detailed experimental comparisons, the competitive performance of the proposed algorithm is verified.

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