Using many objective bat algorithm for solving many-objective nonlinear functions

Document Type : Research Paper


Department of Mathematics, University of Baghdad, Baghdad 00964, Iraq


Despite the fact that algorithmic strategies for dealing with Combinatorial Optimization (CO) have been available for a long time, the further application of Evolutionary Algorithms (EAs) to such problems provides a vehicle for dealing with MOPs of tremendous scope.  BAT Algorithm with Many Objectives several BAT algorithms based on R2 Distance (MaBAT/R2) are described, which blend the predominance notion with the R2 marker technique. While the R2 Indicator simplifies the multi-objective problem (MOP) by rewriting it as a series of Tchebycheff Approach problems, since this leader decision making uses the Tchebycheff Approach as a criterion, tackling these issues at the same time inside the BAT framework may lead to early converging. Predominance is important in constructing the leader's collection because it allows the chosen leaders to encompass fewer dense regions, avoiding local optima and producing a more diverse approximated Pareto front. 9 non-linear standard functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all of the findings (R2020b).

Volume 14, Issue 1
January 2023
Pages 57-65
  • Receive Date: 10 March 2022
  • Revise Date: 24 April 2022
  • Accept Date: 18 May 2022