Hybridization Approach Towards Improving the Performance of Evolutionary Algorithm

Zainab Al Ani, Ashish M. Gujarathi*, G. Reza Vakili-Nezhaad, Ala’a H. Al-Muhtaseb

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Multi-objective differential evolution (MODE) algorithm has been widely used in solving multi-objective optimization problems. In this paper, a hybridization technique is proposed to improve the performance of MODE algorithm in terms of speed and convergence. The proposed hybrid MODE-dynamic-random local search (HMODE-DLS) algorithm combines MODE and dynamic-random local search (DLS) algorithm. To evaluate the proposed algorithm and validate its performance, benchmark test problems (both constrained and non-constrained) are considered to be solved using MODE and the proposed HMODE-DLS algorithms. To compare between the two algorithms, five performance metrices are calculated, which are convergence, spread, generational distance, spacing and hypervolume ratio. Mean and standard deviation values for the performance metrics are reported, and the best in each category is highlighted. The Conv metric results of the new hybrid MODE are compared with other reported ones. Additionally, the effect of local search probability is studied for selected problems. In general, HMODE-DLS performance outshines, in terms of convergence and robustness, compared with other tested algorithms. HMODE-DLS is, generally, faster, and its results are of improved quality compared to MODE algorithm.

Original languageEnglish
Pages (from-to)11065-11086
Number of pages22
JournalArabian Journal for Science and Engineering
Issue number12
Publication statusPublished - Dec 2020


  • Differential evolution
  • Evolutionary algorithms
  • Hybrid algorithms
  • Multi-objective optimization

ASJC Scopus subject areas

  • General


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