Selective Refining Harmony Search: A new optimization algorithm

Shabani, Mahdi; Mirroshandel, Seyed Abolghasem; Asheri, Hadi

doi:10.1016/j.eswa.2017.03.044

Cited by 21 publications

(1 citation statement)

References 45 publications

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“…In [29], the exponential distribution random number was adopted to tune the intermediate solution in harmony memory, and applied in fuzzy logic control optimization problem. Shabani et al in [30] introduced a selective refining and integrating conception for new harmony creation to render the HS algorithm. In [31], an adaptive dynamic HS algorithm based on harmony memory information has been used for optimum design of aircraft panels.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cultural Harmony Search Algorithm for Constrained Optimization Problem of Diesel Blending

et al. 2020

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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 realworld 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.INDEX TERMS Nonlinear diesel blending, variable domain reduction, simplex improved cultural harmony search algorithm, constrained optimization.

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Section: Introductionmentioning

confidence: 99%