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The main objectives of multiobjective evolutionary algorithms are to minimize the distance between the solution set and true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the ability of multiobjective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is a novel enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. This strategy also preserves nondominated solutions in the evolving population and hence gives good convergence. Comparative studies show that the proposed features are effective.