Adaptive control technique to enhance differential multi-objective evolutionary algorithm based on variation rate of quality measures



  • Tran Binh Minh (Corresponding Author) Institute of Information Technology, Academy of Military Science and Technology
  • Nguyen Long National Defense Academy
  • Nguyen Duc Dinh Institute of Information Technology, Academy of Military Science and Technology
  • Thai Trung Kien Institute of Information Technology, Academy of Military Science and Technology



Balancing exploration and exploitation; Search trend; Differential evolutionary algorithm; NSGAII-DE.


When evaluating the multi-objective evolutionary algorithm, they not only focus on the quality of the solution set but also pay attention to the algorithm's ability to explore and exploit because that is the factor ensuring the convergence and diversity of the solution set. Maintaining a balance between exploration and exploitation of the algorithm is a difficult but interesting problem in the research field. In this article, we analyzed the relationship between the quality of the solution set and the algorithm's search efficiency to evaluate trends and propose an adaptive control technique based on the variation rate of quality measures to maintain a better balance between the exploration and exploitation capabilities. Experimental results on the multi-objective evolutionary algorithm using differential direction with some typical benchmark sets are highly competitive, demonstrating the ability to improve the algorithm's efficiency.


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How to Cite

Tran, M., Nguyễn Long, Nguyễn Đức Định, and Thái Trung Kiên. “Adaptive Control Technique to Enhance Differential Multi-Objective Evolutionary Algorithm Based on Variation Rate of Quality Measures”. Journal of Military Science and Technology, vol. 93, no. 93, Feb. 2024, pp. 121-7, doi:10.54939/1859-1043.j.mst.93.2024.121-127.