Multi-objective Narwhal Optimizer: a Novel Algorithm for Multi-Criterion Optimization

Metaheuristic algorithms inspired by natural and biological behaviors have demonstrated strong performance in solving complex optimization problems. However, many existing algorithms are primarily designed for single-objective optimization and, therefore, cannot directly address problems involving multiple conflicting objectives. This paper proposes the Multi-Objective Narwhal Optimizer (MONO), a Pareto-based extension of the recently developed Narwhal Optimizer for solving multi-objective optimization problems. The proposed MONO incorporates Pareto dominance, external archive management, adaptive multi-leader guidance, and crowding-distance-based diversity preservation to effectively balance convergence and exploration throughout the search process. The performance of MONO was evaluated on the CEC benchmark suite and compared with eight representative multi-objective optimization algorithms: NSGA-II, NSGA-III, RVEA, IBEA, SPEA2, MOGWO, MOPSO, and MOEA/D. Experimental evaluation was conducted using three widely adopted performance indicators: Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS). The results demonstrate that MONO achieves improvements of up to 5.8