Many science anti engineering design problems are modeled as constrained multiobjective optimization problem. The major challenges in solving these problems are (i) conflicting objectives and (ii) non linear constraints. These conflicts are responsible for diverging the solution from true Pareto-front. This paper presents a variation of particle swarm optimization algorithm integrated with accelerated exploration technique that adapts to iteration for solving constrained multiobjective optimization problems. Performance of the proposed algorithm is evaluated on standard constrained multiobjective benchmark functions (CEC 2009) and compared with recently proposed DECMOSA algorithm. The comprehensive experimental results show the effectiveness of the proposed algorithm in terms of generation distance, diversity anti convergence metric.