Multi-Objective Optimization Evolutionary Algorithms (MOEAs) belong to heuristic methods proposed for solving Multi-objective Optimization Problems (MOPs). In fact, MOEAs search for a uniformly distributed, near-optimal, and near- complete Pareto front for a given MOP. However, several MOEAs fail to achieve their aim completely due to their fixed population size. To overcome this shortcoming, Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) [20] was proposed. Although DMOEA has the distinction of dynamic population size, it still suffers from a long execution time. To deal with the last disadvantage, we have proposed previously a Parallel Dynamic Multi-Objective Evolutionary Algorithm (PDMOEA) [10] to obtain efficient results in less execution time than the sequential counterparts, in order to tackle more complex problems. This paper is an extended version of [10] and it aims to demonstrate the efficiency of PDMOEA through more experimentations and comparisons. We firstly compare DMOEA with other multi-objective evolutionary algorithms Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II), then we present an exhaustive comparison of PDMOEA versus DMOEA and discuss how the number of used processors influences the efficiency of PDMOEA. As experimental results, PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimizing the computational time, and converging to the desired population size. Finally, the paper establishes a new formula relating the suitable number of processes, required in PDMOEA, and the number of necessary generations to converge to the optimal solutions.

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