Szczegóły publikacji

Opis bibliograficzny

A novel multi-hybrid differential evolution algorithm for optimization of frame structures / Rohit SALGOTRA, Amir H. Gandomi // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322. — 2024 — vol. 14 art. no. 4877, s. 1–28. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 26–27, Abstr. — Publikacja dostępna online od: 2024-02-28. — R. Salgotra - dod. afiliacja: Middle East University, Amman, Jordan


Autorzy (2)


Słowa kluczowe

swarm intelligenceself-adaptive parametershybridizationdifferential evolutionframe structure designnumerical optimization

Dane bibliometryczne

ID BaDAP152552
Data dodania do BaDAP2024-03-26
Tekst źródłowyURL
DOI10.1038/s41598-024-54384-3
Rok publikacji2024
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaScientific Reports

Abstract

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.

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