A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies

Troumbis, Ioannis A.; Tsekouras, George E.; Tsimikas, John V.; Kalloniatis, Christos; Haralambopoulos, Dias

doi:10.1016/j.envsoft.2020.104663

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…Since its inception, differential evolution (DE) has become one of the most commonly used meta-heuristic algorithms for solving optimization problems [ 12 ]. Many scholars have improved DE and applied it in diverse fields, such as clinical medicine [ 13 ], text classification [ 14 ], optics [ 15 ], energy [ 16 ] and neural network [ 17 ]. Improvements studies to DE can be divided into two broad categories: 1) Changes of DE compositions, which enhance the performance of the original DE by improving the mutation, crossover, selection operation and adjusting control parameters; 2) hybrid DE with other meta-heuristic algorithms to improve performance by combing their respective advantages.…”

Section: Introductionmentioning

confidence: 99%

A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

et al. 2021

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Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

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