MODEL: Multi-Objective Differential Evolution with Leadership Enhancement

Differential Evolution (DE) has been successfully used to solve various complex optimization problems; however, it can suffer depending of the complexity of the problem from slow convergence due to its iterative process. The use of the leadership concept was efficiently utilized for the acceleration of Particle Swarm Optimization (PSO) in a single-objective space. The generalization of the leadership concept in multi-objective space is not trivial. Furthermore, despite the efficiency of using the leadership concept, a limited number of multi-objective metaheuristics utilize it. To address these challenges, this paper incorporates the concept of leadership in a multi-objective variant of DE by introducing it into the mutation scheme. The preliminary results are promising as MODEL outperformed the parent algorithm GDE3 and showed the highest accuracy when compared with seven other algorithms.