We introduce the PESO (particle evolutionary swarm optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "m-perturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in particle swarm optimization (PSO) implementations. Constraint handling is based on simple feasibility rules. PESO is compared with respect to a highly competitive technique representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. PESO matches most results and outperforms other PSO algorithms.