Abstract
This paper studies the constrained optimization problem for nonlinear diesel blending. A new hybrid algorithm called cultural harmony search algorithm is presented to solve the proposed optimization problem, which uses cultural knowledge in the belief space of the cultural algorithm to guide the evolving and searching process of the harmony search algorithm. Then, an improved harmony improvisation in the population space of cultural algorithm is developed for new harmony generation to enrich the population diversity. Moreover, in order to accelerate convergence, the domain of decision variables is scaled down by a simplex method at the beginning of the algorithm, and a simplex improved cultural harmony search algorithm is provided. Finally, benchmark functions and the results of application in nonlinear diesel blending of a real-world refinery show the feasibility and effectiveness of the proposed algorithms. The contrasted experiments show that our proposed hybrid algorithm is better than other hybrid algorithms, especially in diesel blending optimization problem.
Highlights
In a typical refinery, a series of operations and units are performed to transform crude oils into various products, where different components of intermediate oils and additives are mixed to yield the refined oil products in diesel blending unit
MAIN RESULTS harmony search (HS) algorithm, cultural harmony search algorithm (CHS), improved cultural harmony search (ICHS) algorithm and simplex improved cultural harmony search (SICHS) algorithm are provided for the better solution of the proposed optimization problem step by step
It aims to seek the harmonic combination of tones which is pleasant to the ear from an aesthetic point of view
Summary
A series of operations and units are performed to transform crude oils into various products, where different components of intermediate oils and additives are mixed to yield the refined oil products in diesel blending unit. The existing algorithms face different difficulties, such as too many parameters to adjust for best results, low accuracy, unreliable performance, local trap, and slow convergence speed To solve this problem, we need a new hybrid algorithm which can quickly search the optimal solution, and keep high precision and stable performance, simultaneously. Because the BS can utilize the knowledge information gathered over the generations to help the PS yield new good solutions to better guide the evolutionary search, a number of hybrid algorithms [39]–[45] in which cultural algorithm is combined with other evolutionary algorithms have been designed to solve different types of optimization problems for better performance. A hybrid algorithm called cultural harmony search (CHS) algorithm in which HS algorithm is combined with cultural algorithm, is developed and further improved for the solution of the proposed optimization problem.
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