Cheetah Optimizer for Multi-objective Optimization Problems

Abstract

Abstract In this paper, a new algorithm named multi-objective cheetah optimizer is presented for solving multi-objective optimization problems. Cheetah optimizer is a new optimization algorithm, which has been proven to be more effective for solving large-scale and complex optimization problems. The proposed MOCO is developed from the single-objective cheetah optimizer by introducing the concepts of non-dominance sorting and archiving. Non-dominance sorting is used to get Pareto optimal solutions. An Archive is used for improving and maintaining their distribution. The experimental results show that the proposed algorithm performs better than the existing multi-objective algorithms in terms of fitness value. The Pareto-optimal fronts exhibit good convergence and coverage. The empirical comparison results of the proposed algorithm with existing multi-objective algorithms exhibit its competitiveness. Simulation studies were performed on well-known multi-objective benchmark functions and real-world engineering design optimization problems to verify the proposed MO algorithm and ensure its applicability in real-life scenarios. Comparative analysis is done for the proposed multi-objective cheetah optimizer and other multi-objective algorithms that have recently been proposed.