Shuffled Complex Evolution based Quantum Particle Swarm Optimization algorithm for mechanical design optimization problems
This paper presents a hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviate as SP-QPSO for solving mechanical constrained design optimization problems. Quantum Particle Swarm Optimization (QPSO) is a variant of standard Particle Swarm Optimization (PSO) algorithm and enhances the local search ability of the algorithm over feasible space. Although QPSO is easier to implement, more intelligent and efficient than the standard PSO, but due to its strategy, the efficiency of the QPSO algorithm's performance in solving complex and high-dimensional problems has decreased. Normalization of constraints is crucial for the efficient permanence of the algorithm. A growing number of researchers have proposed different strategies for handling constraints. For solving constrained optimization problems, Adaptive constraint-handling techniques are extremely promising for solving constrained optimization problems as they use population details during the search to adjust different penalty parameters for each constraint without tuning any type of user defined penalty parameter. In this paper, a novel parameter free adaptive penalty is employed to handle constraints in the search region. The efficiency of SP-QPSO algorithm is numerically investigated using five engineering design problems which have different natures of objective functions, constraints and decision variables with up to six variables and six constraints. The experimental results are analyzed in comparison with those reported in the literature. The computational results show that the proposed algorithm can provide better solutions in terms of efficiency and computational time than traditional PSO and other optimization algorithms.