New methods based on meta-heuristic approaches for solving hard problems have become efficient tools in many disciplines such as engineering, computer science, mathematics, and some other fields. Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. The original version of the quantum evolutionary algorithm (QEA) uses Q-bit individuals in binary code analogous to genes in the conventional genetic algorithm and the concept of quantum computing. Getting stuck in local optimum in some case studies is considered a weakness of primary QEA and makes access to the high standard deviation in statistical analysis. The idea of a combination of two or more methods can create a new approach with augmented features of all the combined methods. QECBO, as a high-level relay hybrid technique, is a new quantum version of the ECBO, which combines ECBO with the property of the quantum particle. QECBO gives quantum particle properties to colliding bodies of solution. The main idea of QECBO originates from the concept of quantum particle behavior in delta potential well and Schrödinger's uncertainty principle addition to Newtonian collision laws. It seems that the QECBO utilizes the rules of uncertainty inside classical mechanics. This paper is devoted to the assessment of the QECBO using some well-known benchmark optimization problems. In addition to less sensitive to population size, the quality of obtained results implies that QECBO utilizes an advanced approach to search solution space based on ECBO and balances exploration and exploitation concepts by using uncertainty laws. The inclination of the agents to the best solution in each iteration could provide a better chance to find an optimal solution. Also, efficient randomization due to uncertainty laws helps the algorithm to escape from the local optimum. Attained statistical results show the QECBO could be a new advanced method that would avoid local optimum and provide an excellent balance between intensification and diversification.